In this paper we investigate several state-of-the-art machine-learning methods for automated classification of clustered microcalcifications (MCs), aimed to assisting radiologists for more accurate diagnosis of breast cancer in a computer-aided diagnosis (CADx) scheme. The methods we consider include: support vector machine (SVM), kernel Fisher discriminant (KFD), and committee machines (ensemble averaging and AdaBoost), most of which have been developed recently in statistical learning theory. We formulate differentiation of malignant from benign MCs as a supervised learning problem, and apply these learning methods to develop the classification algorithms. As input, these methods use image features automatically extracted from clustered MCs. We test these methods using a database of 697 clinical mammograms from 386 cases, which include a wide spectrum of difficult-to-classify cases. We use receiver operating characteristic (ROC) analysis to evaluate and compare the classification performance by the different methods. In addition, we also investigate how to combine information from multiple-view mammograms of the same case so that the best decision can be made by a classifier. In our experiments, the kernel-based methods (i.e., SVM, KFD) yield the best performance, significantly outperforming a well-established CADx approach based on neural network learning.

We have developed a computer-aided detection (CAD) system for breast masses on mammograms. In this study, our purpose was to improve the performance of our mass detection system by using a new dual system approach which combines a CAD system optimized with "average" masses with another CAD system optimized with subtle masses. The latter system is trained to provide high sensitivity in detecting subtle masses. For an unknown mammogram, the two systems are used in parallel to detect suspicious objects. A feed-forward backpropagation neural network trained to merge the scores of the two linear discriminant analysis (LDA) classifiers from the two systems makes the final decision in differentiation of true masses from normal tissue. A data set of 86 patients containing 172 mammograms with biopsy-proven masses was partitioned into a training set and an independent test set. This data set is referred to as the average data set. A second data set of 214 prior mammograms was used for training the second CAD system for detection of subtle masses. When the single CAD system trained on the average data set was applied to the test set, the A_z for false positive (FP) classification was 0.81 and the FP rates were 2.1, 1.5 and 1.3 FPs/image at the case-based sensitivities of 95%, 90% and 85%, respectively. With the dual CAD system, the A_z was 0.85 and the FP rates were improved to 1.7, 1.2 and 0.8 FPs/image at the same case-based sensitivities. Our results indicate that the dual CAD system can improve the performance of mass detection on mammograms.

We present a new algorithm and preliminary results for classifying lesions into BI-RADS shape categories: round, oval, lobulated, or irregular. By classifying masses into one of these categories, computer aided detection (CAD) systems will be able to provide additional information to radiologists. Thus, such a tool could potentially be used in conjunction with a CAD system to enable greater interaction and personalization. For this classification task, we have developed a new set of features using the Beamlet transform, which is a recently developed multi-scale image analysis transform. We trained a k-Nearest Neighbor classifier using images from the Digital Database for Digital Mammography (DDSM). The method was tested on a set of 25 images of each type and we obtained a classification accuracy of 78% for classifying masses as oval or round and an accuracy of 72% for classifying masses as lobulated or round.

Mass detection algorithms generally consist of two stages. The aim of the first stage is to detect all potential masses. In the second stage, the aim is to reduce the false-positives by classifying the detected objects as masses or normal tissue. In this paper, we present a new evidence based, stage-one algorithm for the detection of spiculated masses and architectural distortions. By evidence based, we mean that we use the statistics of the physical characteristics of these abnormalities to determine the parameters of the detection algorithm. Our stage-one algorithm consists of two steps, an enhancement step followed by a filtering step. In the first step, we propose a new technique for the enhancement of spiculations in which a linear filter is applied to the Radon transform of the image. In the second step, we filter the enhanced images with a new class of linear image filters called Radial Spiculation Filters. We have invented these filters specifically for detecting spiculated masses and architectural distortions that are marked by converging lines or spiculations. These filters are highly specific narrowband filters, which are designed to match the expected structures of these abnormalities and form a new class of wavelet-type filterbanks derived from optimal theories of filtering. A key aspect of this work is that each parameter of the filter has been incorporated to capture the variation in physical characteristics of spiculated masses and architectural distortions and that the parameters of the stage-one detection algorithm are determined by the physical measurements.

We introduce a computer-assisted detection (CAD) system for the automated detection of breast masses in screening mammograms. The system targets the directional behavior of the neighborhood pixels surrounding a reference image pixel. The underlying hypothesis is that in the presence of a mass the directional properties of the breast tissue surrounding the mass should be altered. The hypothesis was tested using a database of 1,337 mammographic regions of interest (ROIs) extracted from DDSM mammograms. There were 681 ROIs containing a biopsy-proven mass centered in the ROI (340 malignant, 341 benign) and 656 ROIs depicting normal breast parenchyma. Initially, eight main directional propagations were identified and modeled given the center of the ROI as the reference pixel. Subsequently, eight novel morphological features were extracted for each direction. The features were designed to characterize the disturbance occurring in normal breast parenchyma due to the presence of a mass. Finally, the extracted features were merged using a back propagation neural network (BPANN). The network served as a non linear classifier trained to determine the presence of a mass centered at the reference image pixel. The BPANN was trained and tested using a leave-one-out sampling scheme. Its performance was evaluated with Receiver Operating Characteristics (ROC) analysis. Our CAD system showed an ROC area index of Az=0.88±0.01 for discriminating mass vs. normal ROIs. Detection performance was robust for both malignant (Az=0.88±0.01) and benign masses (Az=0.87±0.01). Thus, the proposed directional neighborhood analysis (DNA) can be applied effectively to identify suspicious masses in screening mammograms.

Breast cancer is second only to lung cancer as a tumor-related cause of death in women. Currently, the method of choice for the early detection of breast cancer is mammography. While sensitive to the detection of breast cancer, its positive predictive value (PPV) is low, resulting in biopsies that are only 15-34% likely to reveal malignancy. This paper explores the use of two novel approaches called Partial Least Squares (PLS) and Kernel-PLS (K-PLS) to the diagnosis of breast cancer. The approach is based on optimization for the partial least squares (PLS) algorithm for linear regression and the K-PLS algorithm for non-linear regression. Preliminary results show that both the PLS and K-PLS paradigms achieved comparable results with three separate support vector learning machines (SVLMs), where these SVLMs were known to have been trained to a global minimum. That is, the average performance of the three separate SVLMs were Az = 0.9167927, with an average partial Az (Az90) = 0.5684283. These results compare favorably with the K-PLS paradigm, which obtained an Az = 0.907 and partial Az = 0.6123. The PLS paradigm provided comparable results. Secondly, both the K-PLS and PLS paradigms out performed the ANN in that the Az index improved by about 14% (Az ≈ 0.907 compared to the ANN Az of ≈ 0.8). The "Press R squared" value for the PLS and K-PLS machine learning algorithms were 0.89 and 0.9, respectively, which is in good agreement with the other MOP values.

We present the results on the development of an automated system for prostate cancer diagnosis and Gleason grading. Images of representative areas of the original Hematoxylin-and-Eosin (H&E)-stained tissue retrieved from each patient, either from a tissue microarray (TMA) core or whole section, were captured and analyzed. The image sets consisted of 367 and 268 color images for the diagnosis and Gleason grading problems, respectively. In diagnosis, the goal is to classify a tissue image into tumor versus non-tumor classes. In Gleason grading, which characterizes tumor aggressiveness, the objective is to classify a tissue image as being from either a low- or high-grade tumor. Several feature sets were computed from the image. The feature sets considered were: (i) color channel histograms, (ii) fractal dimension features, (iii) fractal code features, (iv) wavelet features, and (v) color, shape and texture features computed using Aureon Biosciences' MAGIC system. The linear and quadratic Gaussian classifiers together with a greedy search feature selection algorithm were used. For cancer diagnosis, a classification accuracy of 94.5% was obtained on an independent test set. For Gleason grading, the achieved accuracy of classification into low- and high-grade classes of an independent test set was 77.6%.

Uterine cervical cancer is the second most common cancer among women worldwide. Colposcopy is a diagnostic method, whereby a physician (colposcopist) visually inspects the lower genital tract (cervix, vulva and vagina), with special emphasis on the subjective appearance of metaplastic epithelium comprising the transformation zone on the cervix. Cervical cancer precursor lesions and invasive cancer exhibit certain distinctly abnormal morphologic features. Lesion characteristics such as margin; color or opacity; blood vessel caliber, intercapillary spacing and distribution; and contour are considered by colposcopists to derive a clinical diagnosis. Clinicians and academia have suggested and shown proof of concept that automated image analysis of cervical imagery can be used for cervical cancer screening and diagnosis, having the potential to have a direct impact on improving women’s health care and reducing associated costs. STI Medical Systems is developing a Computer-Aided-Diagnosis (CAD) system for colposcopy -- ColpoCAD. At the heart of ColpoCAD is a complex multi-sensor, multi-data and multi-feature image analysis system. A functional description is presented of the envisioned ColpoCAD system, broken down into: Modality Data Management System, Image Enhancement, Feature Extraction, Reference Database, and Diagnosis and directed Biopsies. The system design and development process of the image analysis system is outlined. The system design provides a modular and open architecture built on feature based processing. The core feature set includes the visual features used by colposcopists. This feature set can be extended to include new features introduced by new instrument technologies, like fluorescence and impedance, and any other plausible feature that can be extracted from the cervical data. Preliminary results of our research on detecting the three most important features: blood vessel structures, acetowhite regions and lesion margins are shown. As this is a new and very complex field in medical image processing, the hope is that this paper can provide a framework and basis to encourage and facilitate collaboration and discussion between industry, academia, and medical practitioners.

The purpose of this study is to develop a computer-aided diagnosis (CAD) system to detect small-sized (from 2mm to 10mm) pulmonary nodules in high resolution helical CT scans. A new CAD system is proposed to locate both juxtapleural nodules and non-pleural nodules. Isotropic resampling and lung segmentation are performed as preprocessing steps. Morphological closing was utilized to smooth the lung contours to include the indented possible juxtapleural locations, thresholding and 3D component analysis were used to obtain 3D volumetric nodule candidates; furthermore, gray level and geometric features were extracted, and analyzed using linear discriminant analysis (LDA) classifier. Leave one case out method was used to evaluate the LDA. To deal with non-pleural nodules, a discrete-time cellular neural network (DTCNN) based on local shape features was developed. This scheme employed the local shape property to perform voxel classification. The shape index feature successfully captured the local shape difference between nodules and non-nodules, especially vessels. To tailor it for lung nodule detection, this DTCNN was trained using genetic algorithms (GAs) to derive the shape index variation pattern of nodules. Nonoverlapping training and testing sets were utilized in the non-pleural nodule detection. 19 clinical thoracic CT cases involving a total of 4838 sectional images were used in this work. The juxtapleural nodule detection method was able to obtain sensitivity 81.25% with an average of 8.29 FPs per case. The non-pleural nodule finding scheme attained sensitivity of 83.9% with an average 3.47 FPs/case. Combining the two subsystems together, an overall performance of 82.98% sensitivity with 11.76 FPs/case can be obtained.

A major challenge in computer-aided diagnostic (CAD) schemes for nodule detection on chest radiographs is the detection of nodules that overlap with ribs. Our purpose was to develop a technique for false-positive reduction in a CAD scheme using a rib-suppression technique based on massive training artificial neural networks (MTANNs). We developed a multiple MTANN (multi-MTANN) consisting of eight MTANNs for removing eight types of false positives. For further removal of false positives caused by ribs, we developed a rib-suppression technique using a multi-resolution MTANN consisting of three different resolution MTANNs. To suppress the contrast of ribs, the multi-resolution MTANN was trained with input chest radiographs and the teaching soft-tissue images obtained by using a dual-energy subtraction technique. Our database consisted of 91 nodules in 91 chest radiographs. With our original CAD scheme based on a difference image technique with linear discriminant analysis, a sensitivity of 82.4% (75/91 nodules) with 4.5 (410/91) false positives per image was achieved. The trained multi-MTANN was able to remove 62.7% (257/410) of false positives with a loss of one true positive. With the rib-suppression technique, the contrast of ribs in chest radiographs was suppressed substantially. Due to the effect of rib-suppression, 41.2% (63/153) of the remaining false positives were removed without a loss of any true positives. Thus, the false-positive rate of our CAD scheme was improved substantially, while a high sensitivity was maintained.

We have been developing computer-aided diagnostic (CAD) scheme to assist radiologists detect lung nodules in thoracic CT images. In order to improve the sensitivity for nodule detection, we developed a selective nodule enhancement filter for nodule which can simultaneously enhance nodules and suppress other normal anatomic structures such as blood vessels and airway walls. Therefore, as preprocessing steps, this filter is useful for improving the sensitivity of nodule detection and for reducing the number of false positives. Another new technique we employed in this study is an automated rule-based classifier. It can significantly reduce the extent of the disadvantages of existing rule-based classifiers, including manual design, poor reproducibility, poor evaluation methods such as re-substitution, and a large overtraining effect. Experimental results performed with Monte Carlo simulation and a real lung nodule CT dataset demonstrated that the automated method can completely eliminate overtraining effect in the procedure of cutoff threshold selection, and thus can minimize overall overtraining effect in the rule-based classifier.

Ground glass nodules (GGNs) have proved especially problematic in lung cancer diagnosis, as despite frequently being malignant they have extremely slow growth rates. In this work, the GGN segmentation results of a computer-based method were compared with manual segmentation performed by two dedicated chest radiologists. CT volumes of 8 patients were acquired by multi-slice CT. 21 pure or mixed GGNs were identified and independently segmented by the computer-based method and by two readers. The computer-based method is initialized by a click point, and uses a Markov random field (MRF) model for segmentation. While the intensity distribution varies for different GGNs, the intensity model used in MRF is adapted for each nodule based on initial estimates. This method was run three times for each nodule using different click points to evaluate consistency. In this work, consistency was defined by the overlap ratio (overlap volume/mean volume). The consistency of the computer-based method with different initial points, with a mean overlap ratio of 0.96±0.02 (95% confidence interval on mean), was significantly higher than the inter-observer consistency between the two radiologists, indicated by a mean overlap ratio of 0.73±0.04. The computer consistency was also significantly higher than the intra-observer consistency of two measurements from the same radiologist, indicated by an overlap ratio of 0.69±0.05 (p-value < 1E-05). The concordance of the computer with the expert interpretation demonstrated a mean overlap ratio of 0.69±0.05. As shown by our data, the consistency provided by the computer-based method is significantly higher than between observers, and the accuracy of the method is no worse than that of one physician’s accuracy with respect to another, allowing more reproducible assessment of nodule growth.

We present a novel technique that provides a case-specific confidence measure for artificial neural network (ANN) based computer-assisted diagnostic (CAD) decisions. The technique relies on the analysis of the feature space neighborhood for each query case and dynamically creates a validation set that allows estimation of a local accuracy of the decisions made by the network. Then a case-specific reliability measure is assigned to each system's response, which can be used to stratify network's predictions according to the acceptable validation error value. The study was performed using a database containing 1,337 mammographic regions of interest (ROIs) with biopsy-proven diagnosis (681 with masses, 656 with normal parenchyma). Two types of neural networks (1) a feed forward network with error back propagation (BPNN) and (2) a generalized regression neural network with RBF nodes (GRNN) were developed to detect masses based on 8 morphological features automatically extracted from each ROI. The performance of the networks was evaluated with Receiver Operating Characteristics (ROC) analysis. The study shows that as the threshold on the acceptable validation error declines, the technique rejects more CAD decisions as not reliable enough. However, the ROC performance for the reliable results steadily improves (from A_z = 0.88 to A_z = 0.98 for BPNN, from A_z = 0.86 to A_z = 0.97 for GRNN). The proposed technique provides a stratification strategy for predictions made by CAD tools and can be applied to any type of decision algorithms.

Measuring the similarity between discretely sampled intensity values of different images as a function of geometric transformations is necessary for performing automatic image registration. Arbitrary spatial transformations require a continuous model for the intensity values of the discrete images. Because of computation cost most researchers choose to use low order basis functions, such as the linear hat function or low order B-splines, to model the discrete images. Using the theory of random processes we show that low order interpolators cause undesirable local optima artifacts in similarity measures based on the L₂ norm, linear correlation coefficient, and mutual information. We show how these artifacts can be significantly reduced, and at times completely eliminated, by using sinc approximating kernels.

We present a set of new methods for efficient and precise registration of any X-Ray modality (fluoroscopy, portal imaging or regular X-Ray imaging) to a CT data set. These methods require neither feature extraction nor 2D or 3D segmentation. Our main contribution is to directly perform the computations on the gradient vector volume of the CT data, which has several advantages. It can increase the precision of the registration as it assesses mainly the alignment of intensity edges in both CT and X-Ray images. By using only significant areas of the gradient vector volume, the amount of information needed in each registration step can be reduced up to a factor of 10. This both speeds up the registration process and allows for using the CT data with full precision, e.g. 512³ voxels. We introduce a Volume Gradient Rendering (VGR) as well as a Volume Gradient Correlation (VGC) method, where the latter one can be used directly for computing the image similarity without DBR generation.

The evaluation of tumor growth as regression under therapy is an important clinical issue. Rigid registration of sequentially acquired 3D-images has proven its value for this purpose. Existing approaches to rigid image registration use the whole volume for the estimation of the rigid transform. Non-rigid soft tissue deformation, however, will imply a bias to the registration result, because local deformations cannot be modeled by rigid transforms. Anatomical substructures, like bones or teeth, are not affected by these deformations, but follow a rigid transform. This important observation is incorporated in the proposed registration algorithm. The selection of anatomical substructure is done by manual interaction of medical experts adjusting the transfer function of the volume rendering software. The parameters of the transfer function are used to identify the voxels that are considered for registration. A rigid transform is estimated by a quaternion gradient descent algorithm based on the intensity values of the specified tissue classes. Commonly used voxel intensity measures are adjusted to the modified registration algorithm. The contribution describes the mathematical framework of the proposed registration method and its implementation in a commercial software package. The experimental evaluation includes the discussion of different similarity measures, the comparison of the proposed method to established rigid registration techniques and the evaluation of the efficiency of the new method. We conclude with the discussion of potential medical applications of the proposed registration algorithm.

The issue of this paper is about real-time or interactive 2D-2D resp. 3D-3D matching. Based on Viola's sample-based stochastic Mutual Information (MI) gradient matching we developed a technique that allows to optimally set all necessary parameters in a short preprocessing step using typical images. In this paper we concentrate on finding an optimal parameter set for Rprop, the underlying stochastic optimizer. The relevant parameters are the start and the minimum learning rate given a pair of aligned images. Rprop is modelled by a Markov chain whose transition kernel is estimated by a mean gradient. We introduce a truncated recursion to simulate Rprop and obtain an expectation for the number of iterations for each parameter combination. This way near optimal parameters are found within 20-50 seconds, depending on the data. Using automatically set parameters for Rprop and the sample size, matching requires 0.3-1.3 s for 2D-2D and 0.6-2.1 s for 3D-3D on our test data using an Athlon 800 MHz processor. Altogether we get a real-time registration algorithm that optimizes its control parameters for the given data within less than a minute.

Much literature on image registration has worked with purely geometric image deformation models. For such models, interpolation/resampling operations are often the computationally intensive steps when iteratively minimizing the deformation cost function. This article discusses some techniques for efficiently implementing and accelerating these operations. To simplify presentation, we discuss our ideas in the context of 2D imaging. However, the concepts readily generalize to 3D. Our central technique is a table-lookup scheme that makes somewhat liberal use of RAM, but should not strain the resources of modern processors if certain design parameters are appropriately selected. The technique works by pre-interpolating and tabulating the grid values of the reference image onto a finer grid along one of the axes of the image. The lookup table can be rapidly constructed using FFTs. Our results show that this technique reduces iterative computation by an order of magnitude. When a minimization algorithm employing coordinate block alternation is used, one can obtain still faster computation by storing certain intermediate quantities as state variables. We refer to this technique as state variable hold-over. When combined with table-lookup, state variable hold-over reduces CPU time by about a factor two, as compared to table-lookup alone.

Nonrigid registration of medical images by maximisation of their mutual information, in combination with a deformation field parameterised by cubic B-splines, has been shown to be robust and accurate in many applications. However, the high computation time is a big disadvantage. This work focusses on the optimisation procedure. Many implementations follow a gradient-descent like approach. The time needed for computing the derivative of the mutual information with respect to the B-spline parameters is the bottleneck in this process. We investigate the influence of several gradient approximation techniques on the number of iterations needed and the computation time per iteration. Three methods are studied: a simple finite difference strategy, the so-called simultaneous perturbation method, and a more analytic computation of the gradient based on a continuous, and differentiable representation of the joint histogram. In addition, the effect of decreasing the number of image samples, used for computing the gradient in each iteration, is investigated. Two types of experiments are performed. Firstly, the registration of an image to itself, after application of a known, randomly generated deformation, is considered. Secondly, experiments are performed with 3D ultrasound brain scans, and 3D CT follow-up scans of the chest. The experiments show that the method using an analytic gradient computation outperforms the other two. Furthermore, the computation time per iteration can be extremely decreased, without affecting the rate of convergence and final accuracy, by using very few samples of the image (randomly chosen every iteration) to compute the derivative. With this approach, large data sets (256³) can be registered within 5 minutes on a moderate PC.

In this paper, we examine the problem of non-rigid, image-to-image registration for CT images of the abdomen. This problem has been previously addressed in many different contexts (e.g., visualization using different imaging modalities, modelling of organ deformation after surgical resection). The particular application in which we are interested is modelling of respiratory motion of abdominal organs, so that we may achieve a more accurate representation of the dose distribution in both targeted structures and non-targeted areas during radiosurgical treatment. Our goal is to register two CT images, each acquired at different positions in the respiratory cycle. We use a transformation model based on B-splines, and take advantage of B-splines' "locality" or "compact support" property to ensure computational efficiency and robust convergence to a satisfactory result. We demonstrate that, although a purely intensity-based registration metric performs well in matching the deformation of the lungs during the respiratory cycle, the movement of other organs (e.g., liver and kidney) is poorly represented due to the poor contrast within and between these organs in the CT images. We introduce a registration metric that is a weighted combination of intensity difference and distance between corresponding points that are manually identified in the two images being registered; we show how the influence of these points can be elegantly added to the metric so that it remains differentiable with respect to the spline control points. Visual inspection reveals that resulting registrations appear to be superior to the intensity-only ones in terms of representation of visceral organ deformation and movement.

In present-day medical practice it is often necessary to nonrigidly align image data, either intra- or inter-patient. Current registration algorithms usually do not take different tissue types into account. A problem that might occur with these algorithms is that rigid tissue, like bone, also deforms elastically. We propose a method to correct a deformation field, that is calculated with a nonrigid registration algorithm. The correction is based on a second feature image, which represents the tissue stiffness. The amount of smoothing of the deformation field is related to this stiffness coefficient. By filtering the deformation field on rigid tissue, the deformation field will represent a local rigid transformation. Other parts of the image containing nonrigid tissue are smoothed less, which leaves the original elastic deformation (almost) untouched. It is shown on a synthetic example and on inspiration-expiration CT data of the thorax, that a filtering of the deformation field based on tissue type indeed keeps rigid tissue rigid, thus improving the registration results.

Breath holding (BH) allows to immobilize organs during radiotherapy treatment of lung cancer. Deformable registration methods applied on 3D Computerized Tomography (CT) scans acquired in BH can be used to evaluate the breath holding reproducibility. Resulting 3D vector fields could then be used to adapt internal margins for each patient. In this work we compare three non-rigid registration schemes with Gaussian, linear-elastic and Nagel-Enckelmann based regularizations. As we do not dispose of gold standard, we analyze vector fields by several operators (transitivity, symmetry, volume dilatation, Jacobian). Experiments were based on clinical data sets of two patients: one with normal lung behavior and second with lung discrepancies which lead to bad BH reproducibility. Results show that none of operators allows to clearly highlight the superiority of a method, except for convergence rapidity and Jacobian.

We introduce an image structure analysis technique suitable in cases where anisotropy plays an important role. The so-called Locally Adapted Scaling Vector Method (LSVM) comprises two steps. First, a procedure to estimate the local main orientation at every point of the image is applied. These orientations are then incorporated in a structure characterization procedure. We apply this methodology to High Resolution Magnetic Resonance Images (HRMRI) of human proximal femoral specimens IN VITRO. We extract a 3D local texture measure to establish correlations with the biomechanical properties of bone specimens quantified via the bone maximum compressive strength. The purpose is to compare our results with the prediction of bone strength using similar isotropic texture measures, bone mineral density, and standard 2D morphometric parameters. Our findings suggest that anisotropic texture measures are superior in cases where directional properties are relevant.

We extend the recently introduced scaling vector method (SVM) to improve the textural characterization of oriented trabecular bone structures in the context of osteoporosis. Using the concept of scaling vectors one obtains non-linear structural information from data sets, which can account for global anisotropies. In this work we present a method which allows us to determine the local directionalities in images by using scaling vectors. Thus it becomes possible to better account for local anisotropies and to implement this knowledge in the calculation of the scaling properties of the image. By applying this adaptive technique, a refined quantification of the image structure is possible: we test and evaluate our new method using realistic two-dimensional simulations of bone structures, which model the effect of osteoblasts and osteoclasts on the local change of relative bone density. The partial differential equations involved in the model are solved numerically using cellular automata (CA). Different realizations with slightly varying control parameters are considered. Our results show that even small changes in the trabecular structures, which are induced by variation of a control parameters of the system, become discernible by applying the locally adapted scaling vector method. The results are superior to those obtained by isotropic and/or bulk measures. These findings may be especially important for monitoring the treatment of patients, where the early recognition of (drug-induced) changes in the trabecular structure is crucial.

Osteoporosis is a metabolic bone disease leading to de-mineralization and increased risk of fracture. The two major factors that determine the biomechanical competence of bone are the degree of mineralization and the micro-architectural integrity. Today, modern imaging modalities (high resolution MRI, micro-CT) are capable of depicting structural details of trabecular bone tissue. From the image data, structural properties obtained by quantitative measures are analysed with respect to the presence of osteoporotic fractures of the spine (in-vivo) or correlated with biomechanical strength as derived from destructive testing (in-vitro). Fairly well established are linear structural measures in 2D that are originally adopted from standard histo-morphometry. Recently, non-linear techniques in 2D and 3D based on the scaling index method (SIM), the standard Hough transform (SHT), and the Minkowski Functionals (MF) have been introduced, which show excellent performance in predicting bone strength and fracture risk. However, little is known about the performance of the various parameters with respect to monitoring structural changes due to progression of osteoporosis or as a result of medical treatment. In this contribution, we generate models of trabecular bone with pre-defined structural properties which are exposed to simulated osteoclastic activity. We apply linear and non-linear texture measures to the models and analyse their performance with respect to detecting architectural changes. This study demonstrates, that the texture measures are capable of monitoring structural changes of complex model data. The diagnostic potential varies for the different parameters and is found to depend on the topological composition of the model and initial “bone density”. In our models, non-linear texture measures tend to react more sensitively to small structural changes than linear measures. Best performance is observed for the 3rd and 4th Minkowski Functionals and for the scaling index method.

The goal of this study was to compare the performance of decision tree (DT) classifiers with artificial neural network (ANN) and linear discriminant analysis (LDA) classifiers under different conditions for the class distributions, feature space dimensionality, and training sample size using a Monte Carlo simulation study. We also investigated a bagging technique for improving the accuracy of the DT. The resubstitution (training) and test accuracies of the classifiers were compared by using the area A_z under the ROC curve as the performance measure. Three types of feature space distributions were studied: the Gaussian feature space, a mixture of Gaussians, and a mixture of uniform distributions. The feature space dimensionality was varied between 2 and 12. In a given experiment, 1000 cases were randomly sampled from each distribution, N_t trainers per class was used for classifier design, and the remaining cases were used to test the classifier. The effect of the training sample size was investigated by varying N_t between 30 and 500. Performance measures from 100 experiments were averaged. Our results indicated that, in the Gaussian feature space, the LDA outperformed the other two classifiers, especially when the number of trainers was low. For the mixture of uniform distributions, the A_z value of the DT was in general higher than that of the ANN and the LDA. For the mixture of Gaussians, the performances of the DT and ANN classifiers were comparable. Our results indicate that a DT can be a viable alternative to ANN and LDA classifiers in certain feature spaces.

Micro-array protein crystal images are now routinely acquired automatically by CCD cameras. High-throughput automatic classification of protein crystals requires to alleviation of the time-consuming task of manual visual inspection. We propose a classification framework combined with a multi-scale image processing method for recognizing protein crystals and precipitates versus clear drops. The main two points of the processing method are the multi-scale Laplacian pyramid filters and histogram analysis techniques to find an effective feature vector. The processing steps include: 1. Tray well cropping using Radon Transform; 2. Droplet cropping using an ellipsoid Hough Transform; 3. Multi-scale image separation with Laplacian pyramidal filters; 4. Feature vector extraction from the histogram of the multi-scale boundary images. The feature vector combines geometric and texture features of each image and provides input to a feed forward binomial neural network classifier. Using human (expert crystallographers) classified images as ground truth, the current experimental results gave 86% true positive and 94% true negative rates (average true percentage is 90%) using an image database which contained over 2,000 images. To enable NESG collaborators to carry our crystal classification, a web-based Matlab server was also developed. Users at other locations on the internet can input micro-array crystal image folders and parameters for training and testing processes through a friendly web interface. Recognition results are shown on the client side website and may be downloaded by a remote user as an Excel spreadsheet file.

Wireless Capsule Endoscopy (WCE) is a new colour imaging technology that enables close examination of the interior of the entire small intestine. Typically, the WCE operates for ~8 hours and captures ~40,000 useful images. The images are viewed as a video sequence, which generally takes a doctor over an hour to analyse. In order to activate certain key features of the software provided with the capsule, it is necessary to locate and annotate the boundaries between certain gastrointestinal (GI) tract regions (stomach, intestine and colon) in the footage. In this paper we propose a method of automatically discriminating stomach, intestine and colon tissue in order to significantly reduce the video assessment time. We use hue saturation chromaticity histograms which are compressed using a hybrid transform, incorporating the Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA). The performance of two classifiers is compared: k-nearest neighbour (kNN) and Support Vector Classifier (SVC). After training the classifier, we applied a narrowing step algorithm to converge to the points in the video where the capsule firstly passes through the pylorus (the valve between the stomach and the intestine) and later the ileocaecal valve (IV, the valve between the intestine and colon). We present experimental results that demonstrate the effectiveness of this method.

Advances in video technology are being incorporated into today’s healthcare practice. For example, colonoscopy is an important screening tool for colorectal cancer. Colonoscopy allows for the inspection of the entire colon and provides the ability to perform a number of therapeutic operations during a single procedure. During a colonoscopic procedure, a tiny video camera at the tip of the endoscope generates a video signal of the internal mucosa of the colon. The video data are displayed on a monitor for real-time analysis by the endoscopist. Other endoscopic procedures include upper gastrointestinal endoscopy, enteroscopy, bronchoscopy, cystoscopy, and laparoscopy. However, a significant number of out-of-focus frames are included in this type of videos since current endoscopes are equipped with a single, wide-angle lens that cannot be focused. The out-of-focus frames do not hold any useful information. To reduce the burdens of the further processes such as computer-aided image processing or human expert’s examinations, these frames need to be removed. We call an out-of-focus frame as non-informative frame and an in-focus frame as informative frame. We propose a new technique to classify the video frames into two classes, informative and non-informative frames using a combination of Discrete Fourier Transform (DFT), Texture Analysis, and K-Means Clustering. The proposed technique can evaluate the frames without any reference image, and does not need any predefined threshold value. Our experimental studies indicate that it achieves over 96% of four different performance metrics (i.e. precision, sensitivity, specificity, and accuracy).

This paper describes a novel framework to build 3D Point Distribution Model (PDM) from a set of segmented volumetric images. This method is based on a deformable model algorithm. Each training sample deforms to approximate all other training shapes. The training sample with best approximation results is then chosen as the template. Finally, the poor approximation results from this template are improved by the "bridge over" scheme, which deforms the template to approximate intermediate training shapes and then deforms the approximations to outliers. The method is applied to construct a 3D PDM of 20 human brain ventricles. The results show that the algorithm leads to more accurate representation than traditional framework. Also, the performance of the PDM of soft tissue is comparable with the PDM of bone structures by a previous method. The traditional framework of deformable model based approach selects the template arbitrarily and deforms the template to approximate training shapes directly. The limitation of the traditional framework is that the representation accuracy of the PDM entirely depends on the direct approximation. Moreover, the arbitrary template selection deteriorates the accuracy of the approximation. Our framework that features template selection and indirect approximation solves the shortcomings and improves the PDM representation accuracy. Furthermore, the "bridge over" framework could be used with any deformable model algorithm. In this sense, the method is a generic framework open to future investigation.

Deformable models are powerful approaches to medical image analysis, particularly segmentation. However, the outcome of applying a parametric or geometric deformable model is often significantly dependent on its initialization. This is an obstacle to robust automatic segmentation. Based on theoretical analyses of the watershed transform, we propose a novel approach to reducing this sensitivity to initialization by deriving a vector field from topographic and Euclidean distance transforms. This vector field is aimed to extend the influence of the gradients at the boundary of the segmentation target over the entire image in a consistent fashion, while ignoring any irrelevant gradients in the original image. Initiated by one or more segmentation seeds, the vector field is first computed using an efficient numerical method, and subsequently participates in the model's evolution process. Integration of the vector field has so far been performed with a two-dimensional (2D) parametric deformable model and with a three-dimensional (3D) geodesic active contour level set model. We believe that our approach will enable a higher degree of automation for deformable-model-based segmentation, particularly in situations where the seeds can be placed automatically based on, for example, a priori knowledge regarding the anatomy and the intensity differentiation between the target and the background. Experiments on segmenting organs and tumors from CT and MR images using the integrated models have shown that this is a promising approach.

Previously, "Deformable organisms" were introduced as a novel paradigm for medical image analysis that uses artificial life modelling concepts. Deformable organisms were designed to complement the classical bottom-up deformable models methodologies (geometrical and physical layers), with top-down intelligent deformation control mechanisms (behavioral and cognitive layers). However, a true physical layer was absent and in order to complete medical image segmentation tasks, deformable organisms relied on pure geometry-based shape deformations guided by sensory data, prior structural knowledge, and expert-generated schedules of behaviors. In this paper we introduce the use of physics-based shape deformations within the deformable organisms framework yielding additional robustness by allowing intuitive real-time user guidance and interaction when necessary. We present the results of applying our physics-based deformable organisms, with an underlying dynamic spring-mass mesh model, to segmenting and labelling the corpus callosum in 2D midsagittal magnetic resonance images.

Rapid and unsupervised quantitative analysis is of utmost importance to ensure clinical acceptance of many examinations using cardiac magnetic resonance imaging (MRI). We present a framework that aims at fulfilling these goals for the application of left ventricular ejection fraction estimation in four-dimensional MRI. The theoretical foundation of our work is the generative two-dimensional Active Appearance Models by Cootes et al., here extended to bi-temporal, three-dimensional models. Further issues treated include correction of respiratory induced slice displacements, systole detection, and a texture model pruning strategy. Cross-validation carried out on clinical-quality scans of twelve volunteers indicates that ejection fraction and cardiac blood pool volumes can be estimated automatically and rapidly with accuracy on par with typical inter-observer variability.

Segmentation and labelling of the left atrium from pre-operative images could be a valuable source of information for the planning of electrophysiology procedures to cure atrial fibrillation. A method is presented that uses multi-slice computed tomography (MSCT) images for this purpose that were initially acquired for coronary assessment. The method combines the power of active shape models (robustness by use of prior anatomical knowledge) with the advantages of solely data driven segmentation methods (accuracy). A triangular shape model was built for the human left atrium and its pulmonary vein trunks. It was automatically adapted to the MSCT images, labelling these structures and segmenting them coarsely. In addition, a segmentation of the blood pool by a Hounsfield threshold was applied to the images. The enclosed volumes were triangulated to get a fine surface representation yet still including many distracting objects (the artery tree, coronaries, adjacent chambers, and bones). A correspondence between surface triangles of the coarse, but anatomically labelled model surface and those of the fine iso-surface was established by a similarity criterion on position and orientation. This allows for the refinement of the model-based segmentation showing more anatomical details by selection of corresponding parts of the iso-surface. Vice versa, the correspondence could be used to assign anatomical labels to each iso-surface patch.

This paper presents a method for automated deformation recovery of the left and right ventricular wall from a time sequence of anatomical images of the heart. The deformation is recovered within the heart wall, i.e. it is not limited only to the epicardium and endocardium. Most of the suggested methods either ignore or approximately model incompressibility of the heart wall. This physical property of the cardiac muscle is mathematically guaranteed to be satisfied by the proposed method. A scheme for decomposition of a complex incompressible geometric transformation into simpler components and its application to cardiac deformation recovery is presented. A general case as well as an application specific solution is discussed. Furthermore, the manipulation of the constructed incompressible transformations, including the computation of the inverse transformation, is computationally inexpensive. The presented method is mathematically guaranteed to generate incompressible transformations which are experimentally shown to be a very good approximation of actual cardiac deformations. The transformation representation has a relatively small number of parameters which leads to a fast deformation recovery. The approach was tested on six sequences of two-dimensional short-axis cardiac MR images. The cardiac deformation was recovered with an average error of 1.1 pixel. The method is directly extendable to three dimensions and to the entire heart.

The original FDK algorithm has been extensively employed in medical and industrial imaging applications. With an increased cone angle, cone beam (CB) artifacts in images reconstructed by the original FDK algorithm deteriorate, since the circular trajectory does not satisfy the so-called data sufficiency condition (DSC). A few “circular plus” trajectories have been proposed in the past to reduce CB artifacts by meeting the DSC. However, the circular trajectory has distinct advantages over other scanning trajectories in practical CT imaging, such as cardiac, vascular and perfusion applications. In addition to looking into the DSC, another insight into the CB artifacts of the original FDK algorithm is the inconsistency between conjugate rays that are 180° apart in view angle. The inconsistence between conjugate rays is pixel dependent, i.e., it varies dramatically over pixels within the image plane to be reconstructed. However, the original FDK algorithm treats all conjugate rays equally, resulting in CB artifacts that can be avoided if appropriate view weighting strategy is exercised. In this paper, a modified FDK algorithm is proposed, along with an experimental evaluation and verification, in which the helical body phantom and a humanoid head phantom scanned by a volumetric CT (64 x 0.625 mm) are utilized. Without extra trajectories supplemental to the circular trajectory, the modified FDK algorithm applies reconstruction-plane-dependent view weighting on projection data before 3D backprojection, which reduces the inconsistency between conjugate rays by suppressing the contribution of one of the conjugate rays with a larger cone angle. Both computer-simulated and real phantom studies show that, up to a moderate cone angle, the CB artifacts can be substantially suppressed by the modified FDK algorithm, while advantages of the original FDK algorithm, such as the filtered backprojection algorithm structure, 1D ramp filtering, and data manipulation efficiency, can be maintained.

C-Arm CT systems suffer from artifacts due to truncated projections caused by a finite detector size. One method used to mitigate the truncation artifacts is projection extrapolation without a priori knowledge. This work focuses on estimating the 0^th and 1^st moments of an image, which can be used to extrapolate a set of truncated projections. If some projections are not truncated, then accurate estimation of the moments can be achieved using only those projections. The more difficult case arises when all projections are truncated by some amount. For this case we make simplifying assumptions and fit the truncated projections with elliptical profiles. From this fit, we estimate the 0^th and 1^st moments of the original image. These estimated moments are then used to perform an extrapolation of the truncated projections, where each projection meets a constraint based on the 0^th and 1^st moments (moment extrapolation). This work focuses on how accurate moment estimates must be for moment extrapolation to be effective. The algorithm was tested on simulated and real data for the head, thorax, and abdomen, and those results were compared to symmetric mirroring by Ohnesorge et al., another extrapolation technique that requires no a priori knowledge. Overall, moment estimation and mass extrapolation alleviates a large amount of image artifact, and can improve on other extrapolation techniques. For the real CT head and abdominal data, the average reconstruction error for mass extrapolation was 48% less than the reconstruction error for symmetric mirroring.

X-ray 3D rotational angiography based on C-arm systems has become a versatile and established tomographic imaging modality for high contrast objects in interventional environment. Improvements in data acquisition, e.g. by use of flat panel detectors, will enable C-arm systems to resolve even low-contrast details. However, further progress will be limited by the incompleteness of data acquisition on the conventional short-scan circular source trajectories. Cone artifacts, which result from that incompleteness, significantly degrade image quality by severe smearing and shading. To assure data completeness a combination of a partial circle with one or several line segments is investigated. A new and efficient reconstruction algorithm is deduced from a general inversion formula based on 3D Radon theory. The method is theoretically exact, possesses shift-invariant filtered backprojection (FBP) structure, and solves the long object problem. The algorithm is flexible in dealing with various circle and line configurations. The reconstruction method requires nothing more than the theoretically minimum length of scan trajectory. It consists of a conventional short-scan circle and a line segment approximately twice as long as the height of the region-of-interest. Geometrical deviations from the ideal source trajectory are considered in the implementation in order to handle data of real C-arm systems. Reconstruction results show excellent image quality free of cone artifacts. The proposed scan trajectory and reconstruction algorithm assure excellent image quality and allow low-contrast tomographic imaging with C-arm based cone-beam systems. The method can be implemented without any hardware modifications on systems commercially available today.

Recently, there has been interest in estimating kinetic model parameters for each voxel in a PET image. To do this, the activity images are first reconstructed from PET sinogram frames at each measurement time, and then the kinetic parameters are estimated by fitting a model to the reconstructed time-activity response of each voxel. However, this indirect approach to kinetic parameter estimation tends to reduce signal-to-noise ratio (SNR) because of the requirement that the sinogram data be divided into individual time frames. In 1985, Carson and Lange proposed, but did not implement, a method based on the EM algorithm for direct parametric reconstruction. More recently, researchers have developed semi-direct methods which use spline-based reconstruction, or direct methods for estimation of kinetic parameters from image regions. However, direct voxel-wise parametric reconstruction has remained a challenge due to the unsolved complexities of inversion and required spatial regularization. In this work, we demonstrate an efficient method for direct voxel-wise reconstruction of kinetic parameters (as a parametric image) from all frames of the PET data. The direct parametric image reconstruction is formulated in a Bayesian framework, and uses the parametric iterative coordinate descent (PICD) algorithm to solve the resulting optimization problem. This PICD algorithm is computationally efficient and allows the physiologically important kinetic parameters to be spatially regularized. Our experimental simulations demonstrate that direct parametric reconstruction can substantially reduce estimation error of kinetic parameters as compared to indirect methods.

In many applications of circular cone-beam CT, it is not uncommon that the size of the field of view (FOV) is smaller than that of the imaging object, thus leading to transverse truncation in projection data. Exact reconstruction in any region is not possible from such truncated data using conventional algorithms. Recently, an exact algorithm for image reconstruction on PI-line segments in helical cone-beam CT has been proposed. This algorithm, which we refer to as the backprojection-filtration (BPF) algorithm, can naturally address the problem of exact region of interest (ROI) reconstruction from such truncated data. In this work, we modified this algorithm to reconstructing images in circular cone-beam scan. The unique property of this modified algorithm is that it can reconstruct exact ROIs in midplane and approximate ROIs in other planes from transversely truncated data. We have performed computer-simulation studies to validate the theoretical assertions. Preliminary results demonstrate that the proposed algorithm provides a solution to the truncation problems caused by limited FOV size.

We present a method to incorporate nonlinear shape prior constraints into segmenting different anatomical structures in medical images. Kernel space density estimation (KSDE) is used to derive the nonlinear shape statistics and enable building a single model for a class of objects with nonlinearly varying shapes. The object contour is coerced by image-based energy into the correct shape sub-distribution (e.g., left or right lung), without the need for model selection. In contrast to an earlier algorithm that uses a local gradient-descent search (susceptible to local minima), we propose an algorithm that iterates between dynamic programming (DP) and shape regularization. DP is capable of finding an optimal contour in the search space that maximizes a cost function related to the difference between the interior and exterior of the object. To enforce the nonlinear shape prior, we propose two shape regularization methods, global and local regularization. Global regularization is applied after each DP search to move the entire shape vector in the shape space in a gradient descent fashion to the position of probable shapes learned from training. The regularized shape is used as the starting shape for the next iteration. Local regularization is accomplished through modifying the search space of the DP. The modified search space only allows a certain amount of deformation of the local shape from the starting shape. Both regularization methods ensure the consistency between the resulted shape with the training shapes, while still preserving DP’s ability to search over a large range and avoid local minima. Our algorithm was applied to two different segmentation tasks for radiographic images: lung field and clavicle segmentation. Both applications have shown that our method is effective and versatile in segmenting various anatomical structures under prior shape constraints; and it is robust to noise and local minima caused by clutter (e.g., blood vessels) and other similar structures (e.g., ribs). We believe that the proposed algorithm represents a major step in the paradigm shift to object segmentation under nonlinear shape constraints.

We have developed an active shape model (ASM)-based segmentation scheme that uses the original Cootes et al. formulation for the underlying mechanics of the ASM but improves the model by fixating selected nodes at specific structural boundaries called transitional landmarks. Transitional landmarks identify the change from one boundary type (such as lung-field/heart) to another (lung-field/diaphragm). This results in a multi-segmented lung-field boundary where each segment correlates to a specific boundary type (lung-field/heart, lung-field/aorta, lung-field/rib-cage, etc.). The node-specified ASM is built using a fixed set of equally spaced feature nodes for each boundary segment. This allows the nodes to learn local appearance models for a specific boundary type, rather than generalizing over multiple boundary types, which results in a marked improvement in boundary accuracy. In contrast, existing lung-field segmentation algorithms based only on ASM simply space the nodes equally along the entire boundary without specification. We have performed extensive experiments using multiple datasets (public and private) and compared the performance of the proposed scheme with other contour-based methods. Overall, the improved accuracy is 3-5 &percent; over the standard ASM and, more importantly, it corresponds to increased alignment with salient anatomical structures. Furthermore, the automatically generated lung-field masks lead to the same fROC for lung-nodule detection as hand-drawn lung-field masks. The accurate landmarks can be easily used for detecting other structures in the lung field. Based on the related landmarks (mediastinum-heart transition, heart-diaphragm transition), we have extended the work to heart segmentation.

Despite the advantages that 3D medical image analysis methods offer and the fast introduction of CT and MRI, to date most hospitals use radiographs to perform preoperative planning of hip surgeries and automatic analysis of hip radiographs is still of interest. In this paper, we present a novel method for segmentation of bone structures in anterior-posterior (AP) radiographs based on Active Appearance Models. The pelvis shape is decomposed in circular regions which reflect convex local arrangement of shape points. A priori global knowledge of the geometric structure of this region representation is captured by a statistical deformable template integrating a set of admissible deformations. The texture of each region is modeled separately, and we build a local Active Appearance Model for each region. A leave-one-out test was used to evaluate the performance of the proposed method and to compare it with conventional Active Appearance Model. The results demonstrate that the method is precise and very robust to large-scale noise present in radiographs, and that it can be useful in the context of preoperative planning of hip surgery.

We propose a new deformable model Deformable Associate Net (DAN). It is represented by a set of nodes which are associated by deformation constrains such as topology association, inter-part association, intra-part association, and geometry to atlas association. Each node in the model is given a priority, and hence DAN is a hierarchical model in which each layer is decided by nodes with same priority. Directional edges and dynamic generated local atlases are used in energy function to incorporate knowledge about tissue and image acquisition. A fast digital topology based method is designed to check whether topology of the model is changed under deformation. The deformation procedure hierarchically combines global and local deformations. Layers with high priority deform first. Once a higher layer is deformed to its target position in an image, the nodes in this layer are fixed, and then used as reference to help lower layers deform to their initial positions. At a particular layer, the model is first deformed by using global affine transformation to fit the image roughly, and then is warped by using a local deformation to fit the image better. The proposed method has been used to segment chest CT images for thoracic surgical planning, and it is also promising for other medical applications, such as model based image registration, and model-based 3D modeling.

This paper describes a method for automatic contour detection in reformatted short-axis (SA) cardiac computed tomography (CT) using a virtual exploring robot. The robot is a tricycle with a steering front wheel. Its motion obeys a set of kinematic equations and is subject to the non-holonomic constraints. The robot is designed to navigate in the binary representation of a cardiac image, consisting of the allowed navigational and obstacle spaces. It is initially positioned inside the allowed navigational space. Avoiding obstacles, the robot autonomously cruises through the navigational space and collects information about the location of the left ventricular (LV) boundaries. Consequently, the obtained information is used to reconstruct the endocardial and epicardial contours. Validation of the method was performed on in-vivo multislice multiphase short-axis cardiac CT images of ten subjects. Results showed good correlation between the quantitative parameters, computed from manual and automatic segmentation: for end-diastolic volume (EDV) r=0.99, for end-systolic volume (ESV) r=0.98, ejection fraction (EF) r=0.83, and LV mass (LVM) r=0.95.

The presence of speckle (a spatial stochastic process in an ultrasound image) makes ultrasound segmentation difficult. Speckle introduces local minima in the MAP energy function of an active contour, and when evolving under gradient descent, the contour gets trapped in a spurious local minimum. In this paper, we propose an alternate technique for evolving a MAP active contour. The technique has two parts: a deterministic evolution strategy called tunneling descent which escapes from spurious local minima, and a stopping rule for terminating the evolution. The combination gives an algorithm that is robust and gives good segmentations. The algorithm also benefits from having only a few free parameters which do not require tweaking. We present the conceptual framework of the algorithm in this paper, and study the impact of different stopping rules on the performance of the algorithm. The algorithm is used to segment the endocardium in cardiac ultrasound images. We present segmentation results in this paper and an experimental evaluation of different stopping rules on the performance of the algorithm. Although the algorithm is presented as an ultrasound segmentation technique, in fact, it can be used to segment any first-order texture boundary.

The efficacy of image processing and analysis on skull stripped MR images vis-a-vis the original images is well established. Additionally, compliance with the Health Insurance Portability and Accountability Act (HIPAA) requires neuroimage repositories to anonymise the images before sharing them. This makes the non-trivial skull stripping process all the more significant. While a number of optimal approaches exist to strip the skull from T1-weighted MR images to the best of our knowledge, there is no simple, robust, fast, parameter free and fully automatic technique to perform the same on T2-weighted images. This paper presents a strategy to fill this gap. It employs a fast parameterization of the T2 image intensity onto a standardized T1 intensity scale. The parametric "T1-like" image obtained via the transformation, which takes only a few seconds to compute, is subsequently processed by any of the many T1-based brain extraction techniques to derive the brain mask. Masking the original T2 image with this brain mask strips the skull. By standardizing the intensity of the parametric image, preset algorithm-specific parameters (if any) could be used across multiple datasets. The proposed scheme has been used in a number of phantom and clinical T2 brain datasets to successfully strip the skull.

In vivo studies of the relationships that exist among vascular geometry, plaque morphology, and hemodynamics have recently been made possible through the development of a system that accurately reconstructs coronary arteries imaged by x-ray angiography and intravascular ultrasound (IVUS) in three dimensions. Currently, the bottleneck of the system is the segmentation of the IVUS images. It is well known that IVUS images contain numerous artifacts from various sources. Previous attempts to create automated IVUS segmentation systems have suffered from either a cost function that does not include enough information, or from a non-optimal segmentation algorithm. The approach presented in this paper seeks to strengthen both of those weaknesses -- first by building a robust, knowledge-based cost function, and then by using a fully optimal, three-dimensional segmentation algorithm. The cost function contains three categories of information: a compendium of learned border patterns, information theoretic and statistical properties related to the imaging physics, and local image features. By combining these criteria in an optimal way, weaknesses associated with cost functions that only try to optimize a single criterion are minimized. This cost function is then used as the input to a fully optimal, three-dimensional, graph search-based segmentation algorithm. The resulting system has been validated against a set of manually traced IVUS image sets. Results did not show any bias, with a mean unsigned luminal border positioning error of 0.180 ± 0.027 mm and an adventitial border positioning error of 0.200 ± 0.069 mm.

Coupling the geodesic active contours model with statistical information based on regions introduces robustness in the segmentation of images with weak or inhomogeneous gradients. In the estimation of the probability density function for each region take part the definition of the features that describe the image inside the different regions and the method of density estimation itself. A Gaussian Mixture Model is frequently proposed for density estimation. This approach is based on the assumption that the intensity distribution of the image is the most discriminant feature in a region. However, the use of second order features provides a better discrimination of the different regions, as these features represent more accurately the local properties of the image manifold. Due to the high dimensionality of the problem, the use of non parametric density estimation methods becomes necessary. In this article, we present a novel method of introducing the second order information of an image for non parametric estimation of the probability density functions of the different tissues that are present in medical images. The novelty of the method stems on the use of the response of the image under an orthogonal harmonic operator set projected onto a prototype space for feature generation. The technique described here is applied to the segmentation of brain aneurysms in Computed Tomography Angiography (CTA) and 3D Rotational Angiography (3DRA) showing a qualitative improvement from the Gaussian Mixture Model approach.

Tumor vasculature has a high degree of irregularity as compared to normal vasculature. The quantification of the morphometric complexity in tumor images can be useful in diagnosis. Also, it is desirable in several other medical applications to have an automated complexity analysis to aid in diagnosis and prognosis under treatment. e.g. in diabetic retinopathy and in arteriosclerosis. In addition, prior efforts at segmentation of the tumor vasculature using matched filtering, template matching and splines have been hampered by the irregularity of these vessels. We try to solve both problems by introducing a novel technique for vessel detection, followed by a tracing-independent complexity analysis based on a combination of ideas. First, the vessel cross-sectional profile is modeled using a continuous and everywhere differentiable family of super-Gaussian curves. This family generates rectangular profiles that can accurately localize the vessel boundaries in microvasculature images. Second, a robust non-linear regression algorithm based on M-estimators is used to estimate the parameters that optimally characterize the vessel’s shape. A framework for the quantitative analysis of the complexity of the vasculature based on the vessel detection is presented. A set of measures that quantify the complexity are proposed viz. Squared Error, Entropy-based and Minimum Description Length-based Shape Complexities. They are completely automatic and can deal with complexities of the entire vessel unlike existing tortuousity measures which deal only with vessel centerlines. The results are validated using carefully constructed phantom and real image data with ground truth information from an expert observer.

We introduce a novel node-structural map method for digital image-based analysis of angiogenesis, and apply it to quantitate the effect of pro-angiogenic (e.g. VEGF) and anti-angiogenic (e.g. anti-VEGF) treatments on blood vessel patterns in the quail chorioallantoic membrane (CAM) in vivo model. After vessel segmentation and skeletonization, a node-structural map is derived to represent the structural property of each pixel in the skeletonized image, such as end nodes (root and leaf), branch and furcation/junction nodes. By combining the node-structural map with vascular thickness map, our proposed method provides more detailed morphometric and structural measurement of vascular angiogenesis, such as average diameter, average branch length, branch thickness distribution, density of branch and furcation nodes, etc. Furthermore, the concept of vascular generations will be proposed. Accordingly, more detailed measurements related to the generations of the vascular tree can be easily evaluated. From the quantitative analysis results, it correctly shows the fact that VEGF/anti-VEGF can modify the blood vessel pattern and increase/decrease the vessel density in CAM significantly, compared to the phosphate-buffered saline treated controls.

Segmentation of medical images is fundamental for many high-level applications. Unsupervised techniques such as region growing or merging allow automated processing of large data amounts. The regions are usually described by a mean feature vector, and the merging decisions are based on the Euclidean distance. This kind of similarity model is strictly local, since the feature vector of each region is calculated without evaluating the region's surrounding. Therefore, region merging often fails to extract visually comprehensible and anatomically relevant regions. In our approach, the local model is extended. Regional similarity is calculated for a pair of adjacent regions, e.g. considering the contrast along their common border. Global similarity components are obtained by analyzing the entire image partitioning before and after a hypothetical merge. Hierarchical similarities are derived from the iteration history. Local, regional, global, and hierarchical components are combined task-specifically guiding the iterative region merging process. Starting with an initial watershed segmentation, the process terminates when the entire image is represented as a single region. A complete segmentation takes only a few seconds. Our approach is evaluated contextually on plain radiographs that display human hands acquired for bone age determination. Region merging based on a local model fails to detect most bones, while a correct localization and delineation is obtained with the combined model. A gold standard is computed from ten manual segmentations of each radiograph to evaluate the quality of delineation. The relative error of labeled pixels is 15.7%, which is slightly more than the mean error of the ten manual references to the gold standard (12%). The flexible and powerful similarity model can be adopted to many other segmentation tasks in medical imaging.