Scoliosis is a highly prevalent spine deformity that has traditionally been diagnosed through measurement of the Cobb
angle on radiographs. More recent technology such as the commercial EOS imaging system, although more accurate, also
require manual intervention for selecting the extremes of the vertebrae forming the Cobb angle. This results in a high
degree of inter and intra observer error in determining the extent of spine deformity. Our primary focus is to eliminate the
need for manual intervention by robustly quantifying the curvature of the spine in three dimensions, making it consistent
across multiple observers. Given the vertebrae centroids, the proposed Vertebrae Sequence Angle (VSA) estimation and
segmentation algorithm finds the largest angle between consecutive pairs of centroids within multiple inflection points on
the curve. To exploit existing clinical diagnostic standards, the algorithm uses a quasi-3-dimensional approach considering
the curvature in the coronal and sagittal projection planes of the spine. Experiments were performed with manuallyannotated
ground-truth classification of publicly available, centroid-annotated CT spine datasets. This was compared with
the results obtained from manual Cobb and Centroid angle estimation methods. Using the VSA, we then automatically
classify the occurrence and the severity of spine curvature based on Lenke’s classification for idiopathic scoliosis. We
observe that the results appear promising with a scoliotic angle lying within ± 9° of the Cobb and Centroid angle, and
vertebrae positions differing by at the most one position. Our system also resulted in perfect classification of scoliotic from
healthy spines with our dataset with six cases.
Protein crystallization plays a crucial role in pharmaceutical research by supporting the investigation of a protein’s molecular structure through X-ray diffraction of its crystal. Due to the rare occurrence of crystals, images must be manually inspected, a laborious process. We develop a solution incorporating a regularized, logistic regression model for automatically evaluating these images. Standard image features, such as shape context, Gabor filters and Fourier transforms, are first extracted to represent the heterogeneous appearance of our images. Then the proposed solution utilizes Elastic Net to select relevant features. Its <i>L</i><sup>1</sup>-regularization mitigates the effects of our large dataset, and its <i>L</i><sup>2</sup>- regularization ensures proper operation when the feature number exceeds the sample number. A two-tier cascade classifier based on naïve Bayes and random forest algorithms categorized the images. In order to validate the proposed method, we experimentally compare it with naïve Bayes, linear discriminant analysis, random forest, and their two-tier cascade classifiers, by 10-fold cross validation. Our experimental results demonstrate a 3-category accuracy of 74%, outperforming other models. In addition, Elastic Net better reduces the false negatives responsible for a high, domain specific risk. To the best of our knowledge, this is the first attempt to apply Elastic Net to classifying protein crystallization images. Performance measured on a large pharmaceutical dataset also fared well in comparison with those presented in the previous studies, while the reduction of the high-risk false negatives is promising.
Interactive segmentation algorithms such as GrowCut usually require quite a few user interactions to
perform well, and have poor repeatability. In this study, we developed a novel technique to boost the
performance of the interactive segmentation method GrowCut involving: 1) a novel "focused
sampling" approach for supervised learning, as opposed to conventional random sampling; 2)
boosting GrowCut using the machine learned results. We applied the proposed technique to the
glioblastoma multiforme (GBM) brain tumor segmentation, and evaluated on a dataset of ten cases
from a multiple center pharmaceutical drug trial. The results showed that the proposed system has
the potential to reduce user interaction while maintaining similar segmentation accuracy.
This paper presents a feature-based image registration framework which exploits a novel machine learning
(ML)-based interest point detection (IPD) algorithm for feature selection and correspondence detection. We use a feed-forward
neural network (NN) with back-propagation as our base ML detector. Literature on ML-based IPD is scarce and
to our best knowledge no previous research has addressed feature selection strategy for IPD purpose with cross-validation
(CV) detectability measure. Our target application is the registration of clinical abdominal CT scans with
abnormal anatomies. We evaluated the correspondence detection performance of the proposed ML-based detector
against two well-known IPD algorithms: SIFT and SURF. The proposed method is capable of performing affine rigid
registrations of 2D and 3D CT images, demonstrating more than two times better accuracy in correspondence detection
than SIFT and SURF. The registration accuracy has been validated manually using identified landmark points. Our
experimental results shows an improvement in 3D image registration quality of 18.92% compared with affine
transformation image registration method from standard ITK affine registration toolkit.
It is a challenging task to automatically segment glioblastoma multiforme (GBM) brain tumors on T1w post-contrast
isotropic MR images. A semi-automated system using fuzzy connectedness has recently been developed for computing
the tumor volume that reduces the cost of manual annotation. In this study, we propose a an ensemble method that
combines multiple segmentation results into a final ensemble one. The method is evaluated on a dataset of 20 cases from
a multi-center pharmaceutical drug trial and compared to the fuzzy connectedness method. Three individual methods
were used in the framework: fuzzy connectedness, GrowCut, and voxel classification. The combination method is a
confidence map averaging (CMA) method. The CMA method shows an improved ROC curve compared to the fuzzy
connectedness method (p < 0.001). The CMA ensemble result is more robust compared to the three individual methods.
This paper presents a novel cell counting system which exploits the Fast Radial Symmetry Transformation (FRST)
algorithm . The driving force behind our system is a research on neurogenesis in the intact nervous system of
<i>Manduca Sexta</i> or the Tobacco Hornworm, which was being studied to assess the impact of age, food and environment
on neurogenesis. The varying thickness of the intact nervous system in this species often yields images with
inhomogeneous background and inconsistencies such as varying illumination, variable contrast, and irregular cell size.
For automated counting, such inhomogeneity and inconsistencies must be addressed, which no existing work has done
successfully. Thus, our goal is to devise a new cell counting algorithm for the images with non-uniform background. Our
solution adapts FRST: a computer vision algorithm which is designed to detect points of interest on circular regions such
as human eyes. This algorithm enhances the occurrences of the stained-cell nuclei in 2D digital images and negates the
problems caused by their inhomogeneity. Besides FRST, our algorithm employs standard image processing methods,
such as mathematical morphology and connected component analysis. We have evaluated the developed cell counting
system with fourteen digital images of Tobacco Hornworm's nervous system collected for this study with ground-truth
cell counts by biology experts. Experimental results show that our system has a minimum error of 1.41% and mean error
of 16.68% which is at least forty-four percent better than the algorithm without FRST.
Inconsistency and a lack of reproducibility are commonly associated with semi-automated segmentation methods. In this
study, we developed an ensemble approach to improve reproducibility and applied it to glioblastoma multiforme (GBM)
brain tumor segmentation on T1-weigted contrast enhanced MR volumes. The proposed approach combines samplingbased
simulations and ensemble segmentation into a single framework; it generates a set of segmentations by perturbing
user initialization and user-specified internal parameters, then fuses the set of segmentations into a single consensus
result. Three combination algorithms were applied: majority voting, averaging and expectation-maximization (EM). The
reproducibility of the proposed framework was evaluated by a controlled experiment on 16 tumor cases from a multicenter
drug trial. The ensemble framework had significantly better reproducibility than the individual base Otsu
thresholding method (p<.001).
This study applied a Gaussian Mixture Model (GMM) to apparent diffusion coefficient (ADC) histograms to evaluate
glioblastoma multiforme (GBM) tumor treatment response using diffusion weighted (DW) MR images. ADC mapping,
calculated from DW images, has been shown to reveal changes in the tumor's microenvironment preceding
morphologic tumor changes. In this study, we investigated the effectiveness of features that represent changes from
pre- and post-treatment tumor ADC histograms to detect treatment response. The main contribution of this work is to
model the ADC histogram as the composition of two components, fitted by GMM with expectation maximization (EM)
algorithm. For both pre- and post-treatment scans taken 5-7 weeks apart, we obtained the tumor ADC histogram,
calculated the two-component features, as well as the other standard histogram-based features, and applied supervised
learning for classification. We evaluated our approach with data from 85 patients with GBM under chemotherapy, in
which 33 responded and 52 did not respond based on tumor size reduction. We compared AdaBoost and random
forests classification algorithms, using ten-fold cross validation, resulting in a best accuracy of 69.41%.
This paper presents an experimental study for assessing the applicability of general-purpose 3D segmentation algorithms for analyzing dental periapical lesions in cone-beam computed tomography (CBCT) scans. In the field of Endodontics, clinical studies have been unable to determine if a periapical granuloma can heal with non-surgical methods. Addressing this issue, Simon et al. recently proposed a diagnostic technique which non-invasively classifies target lesions using CBCT. Manual segmentation exploited in their study, however, is too time consuming and unreliable for real world adoption. On the other hand, many technically advanced algorithms have been proposed to address segmentation problems in various biomedical and non-biomedical contexts, but they have not yet been applied to the field of dentistry. Presented in this paper is a novel application of such segmentation algorithms to the clinically-significant dental problem. This study evaluates three state-of-the-art graph-based algorithms: a normalized cut algorithm based on a generalized eigen-value problem, a graph cut algorithm implementing energy minimization techniques, and a random walks algorithm derived from discrete electrical potential theory. In this paper, we extend the original 2D formulation of the above algorithms to segment 3D images directly and apply the resulting algorithms to the dental CBCT images. We experimentally evaluate quality of the segmentation results for 3D CBCT images, as well as their 2D cross sections. The benefits and pitfalls of each algorithm are highlighted.