Accurate and reproducible tissue identification techniques are essential for understanding structural and functional changes that either occur naturally with aging, or because of chronic disease, or in response to intervention therapies. These image analysis techniques are frequently utilized for characterization of changes in bone architecture to assess fracture risk, and for the assessment of loss of muscle mass and strength defined as sarcopenia. Peripheral quantitative computed tomography (pQCT) is widely employed for tissue identification and analysis. Advantages of pQCT scanners are compactness, portability, and low radiation dose. However, these characteristics imply limitations in spatial resolution and SNR. Therefore, there is still a need for segmentation methods that address image quality limitations and artifacts such as patient motion. In this paper, we introduce multi-atlas segmentation (MAS) techniques to identify soft and hard tissues in pQCT scans of the proximal tibia (~ 66% of tibial length) and to address the above factors that limit delineation accuracy. To calculate the deformation fields, we employed multi-grid free-form deformation (FFD) models with B-splines and a symmetric extension of the log-domain diffeomorphic demons (SDD). We then applied majority voting and Simultaneous Truth And Performance Level Estimation (STAPLE) for label fusion. We compared the results of our MAS methodology for each deformable registration model and each label fusion method, using Dice similarity coefficient scores (DSC). The results show that our technique utilizing SDD with STAPLE produces very good accuracy (DSC mean of 0.868) over all tissues, even for scans with considerable quality degradations caused by motion artifacts.
The analysis and characterization of imaging patterns is a significant research area with several applications to biomedicine, remote sensing, homeland security, social networking, and numerous other domains. In this paper we study and develop mathematical methods and algorithms for disease diagnosis and tissue characterization. The central hypothesis is that we can predict the occurrence of diseases with a certain level of confidence using supervised learning techniques that we apply to medical imaging datasets that include healthy and diseased subjects. We develop methods for calculation of sparse representations to classify imaging patterns and we explore the advantages of this technique over traditional texture-based classification. We introduce integrative sparse classifier systems that utilize structural block decomposition to address difficulties caused by high dimensionality. We propose likelihood functions for classification and decision tuning strategies. We performed osteoporosis classification experiments on the TCB challenge dataset. TCB contains digital radiographs of the calcaneus trabecular bone of 87 healthy and 87 osteoporotic subjects. The scans of healthy and diseased subjects show little or no visual differences, and their density histograms have significant overlap. We applied 30-fold crossvalidation to evaluate the classification performances of our methods, and compared them to a texture based classification system. Our results show that ensemble sparse representations of imaging patterns provide very good separation between groups of healthy and diseased subjects and perform better than conventional sparse and texture-based techniques.
We describe a systematic approach to image, track, and quantify the movements of HIV viruses embedded in human cervical mucus. The underlying motivation for this study is that, in HIV-infected adults, women account for more than half of all new cases and most of these women acquire the infection through heterosexual contact. The endocervix is believed to be a susceptible site for HIV entry. Cervical mucus, which coats the endocervix, should play a protective role against the viruses. Thus, we developed a methodology to apply time-resolved confocal microscopy to examine the motion of HIV viruses that were added to samples of untreated cervical mucus. From the images, we identified the viruses, tracked them over time, and calculated changes of the statistical mean-squared displacement (MSD) of each virus. Approximately half of tracked viruses appear constrained while the others show mobility with MSDs that are proportional to τα+ν2τ2, over time range τ, depicting a combination of anomalous diffusion (0<α<0.4) and flow-like behavior. The MSD data also reveal plateaus attributable to possible stalling of the viruses. Although a more extensive study is warranted, these results support the assumption of mucus being a barrier against the motion of these viruses.
Peripheral Quantitative Computed Tomography (pQCT) is a non-invasive imaging technology that is well-suited for quantification of bone structural and material properties. Because of its increasing use and applicability, the development of automated quantification methods for pQCT images is an appealing field of research. In this paper we introduce a software system for hard and soft tissue quantification in the lower leg using pQCT imaging data. The main stages of our approach are the segmentation and identification of bone, muscle and fat, and the computation of densitometric and geometric variables of each regional tissue type. Our system was validated against reference area and densitometric measurements over a set of test images and produced encouraging results.
Hypodense metastases are not always completely distinguishable from benign cysts in the liver using conventional
Computed Tomography (CT) imaging, since the two lesion types present with overlapping intensity distributions
due to similar composition as well as other factors including beam hardening and patient motion. This problem
is extremely challenging for small lesions with diameter less than 1 cm. To accurately characterize such lesions,
multiple follow-up CT scans or additional Positron Emission Tomography or Magnetic Resonance Imaging exam
are often conducted, and in some cases a biopsy may be required after the initial CT finding. Gemstone
Spectral Imaging (GSI) with fast kVp switching enables projection-based material decomposition, offering the
opportunity to discriminate tissue types based on their energy-sensitive material attenuation and density. GSI
can be used to obtain monochromatic images where beam hardening is reduced or eliminated and the images
come inherently pre-registered due to the fast kVp switching acquisition. We present a supervised learning
method for discriminating between cysts and hypodense liver metastases using these monochromatic images.
Intensity-based statistical features extracted from voxels inside the lesion are used to train optimal linear and
nonlinear classifiers. Our algorithm only requires a region of interest within the lesion in order to compute
relevant features and perform classification, thus eliminating the need for an accurate segmentation of the lesion.
We report classifier performance using M-fold cross-validation on a large lesion database with radiologist-provided
lesion location and labels as the reference standard. Our results demonstrate that (a) classification using a single
projection-based spectral CT image, i.e., a monochromatic image at a specified keV, outperforms classification
using an image-based dual energy CT pair, i.e., low and high kVp images derived from the same fast kVp
acquisition and (b) classification using monochromatic images can achieve very high accuracy in separating
benign liver cysts and metastases, especially for small lesions.
The topic of aerial image registration attracts considerable interest within the imaging research community due to its significance for several applications, including change detection, sensor fusion, and topographic mapping. Our interest is focused on finding the optimal transformation between two aerial images that depict the same visual scene in the presence of pronounced spatial, temporal, and sensor variations. We first introduce a stochastic edge estimation process suitable for geometric shape-based registration, which we also compare to intensity-based registration. Furthermore, we propose an objective function that weights the L2 distances of the edge estimates by the feature points' energy, which we denote by sum of normalized squared differences and compare to standard objective functions, such as mutual information and the sum of absolute centered differences. In the optimization stage, we employ a genetic algorithm scheme in a multiscale image representation scheme to enhance the registration accuracy and reduce the computational load. Our experimental tests, measuring registration accuracy, rate of convergence, and statistical properties of registration errors, suggest that the proposed edge-based representation and objective function in conjunction with genetic algorithm optimization are capable of addressing several forms of imaging variations and producing encouraging registration results.
The detection and classification of objects in complicated backgrounds represents a difficult image analysis problem. Previous methods have employed additional information from dynamic scene processing to extract the object of interest from its environment and have produced efficient results. However, the study of object detection based on the information provided uniquely by still images has not been comprehensively studied. In this work, a different approach is proposed, when dynamic information is not available for detection. The presented scheme consists of two main stages. The first one includes a still image segmentation approach that makes use of multi-scale information and graph-based grouping to partition the image scene into meaningful regions. This is followed by a texture-based classification algorithm, in which correspondence analysis is used for feature selection and optimisation purposes. The outcomes of this methodology provide representative results at each stage of the study, to indicate the efficiency and potential of this approach for classification/detection in the difficult task of object detection in camouflaged environments.
An automated multiscale segmentation approach for color images is presented. The scale-space stack is generated using the Perona-Malik diffusion approach and the watershed algorithm is employed to produce the regions at each scale. A minima-linking process by downward projection is carried out over the successive scales, and a region dissimilarity measure—combining scale, contrast, and homogeneity—is subsequently estimated on the finer scale (localization scale). The dissimilarity measure is estimated as a function of two different features, i.e., the dynamics of contours and the relative entropy of color region distributions, combined by means of a fuzzy-rule-based system. A region-merging process is also applied to the localization scale to produce the final regions. To validate the performance of the proposed multiscale segmentation, qualitative and quantitative results are provided in comparison to its single-scale counterpart. We also deal with the topic of localization scale selection. This stage is critical for the final segmentation results and can be used as a preprocessing step for higher level computer vision applications as well. A preliminary study of localization scale selection techniques is carried out. A scale selection method that originates from the evolution of the probability distribution of a region homogeneity measure across the generated scales is proposed next. The proposed algorithm is finally compared to a previously reported approach to indicate its efficiency.
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