Detection of malignancy from histopathological images of breast cancer is a labor-intensive and error-prone
process. To streamline this process, we present an efficient Computer Aided Diagnostic system that can differentiate
between cancerous and non-cancerous H&E (hemotoxylin&eosin) biopsy samples. Our system uses novel
textural, topological and morphometric features taking advantage of the special patterns of the nuclei cells in
breast cancer histopathological images. We use a Support Vector Machine classifier on these features to diagnose
malignancy. In conjunction with the maximum relevance - minimum redundancy feature selection technique, we
obtain high sensitivity and specificity. We have also investigated the effect of image compression on classification
performance.
We present a computationally efficient method for analyzing H&E stained digital pathology slides with the objective of
discriminating diagnostically relevant vs. irrelevant regions. Such technology is useful for several applications: (1) It can
speed up computer aided diagnosis (CAD) for histopathology based cancer detection and grading by an order of magnitude
through a triage-like preprocessing and pruning. (2) It can improve the response time for an interactive digital pathology
workstation (which is usually dealing with several GByte digital pathology slides), e.g., through controlling adaptive
compression or prioritization algorithms. (3) It can support the detection and grading workflow for expert pathologists in a
semi-automated diagnosis, hereby increasing throughput and accuracy. At the core of the presented method is the statistical
characterization of tissue components that are indicative for the pathologist's decision about malignancy vs. benignity,
such as, nuclei, tubules, cytoplasm, etc. In order to allow for effective yet computationally efficient processing, we propose
visual descriptors that capture the distribution of color intensities observed for nuclei and cytoplasm. Discrimination
between statistics of relevant vs. irrelevant regions is learned from annotated data, and inference is performed via linear
classification. We validate the proposed method both qualitatively and quantitatively. Experiments show a cross validation
error rate of 1.4%. We further show that the proposed method can prune ≈90% of the area of pathological slides while
maintaining 100% of all relevant information, which allows for a speedup of a factor of 10 for CAD systems.
We propose a new method of classifying the local structure types, such as nodules, vessels, and junctions, in thoracic CT scans. This classification is important in the context of computer aided detection (CAD) of lung nodules. The proposed method can be used as a post-process component of any lung CAD system. In such a scenario, the classification results provide an effective means of removing false positives caused by vessels and junctions thus improving overall performance. As main advantage, the proposed solution transforms the complex problem of classifying various 3D topological structures into much simpler 2D data clustering problem, to which more generic and flexible solutions are available in literature, and
which is better suited for visualization. Given a nodule candidate, first, our solution robustly fits an anisotropic Gaussian to the data. The resulting Gaussian center and spread parameters are used to affine-normalize the data domain so as to warp the fitted anisotropic ellipsoid into a fixed-size isotropic sphere. We propose an automatic method to extract a 3D spherical manifold, containing the appropriate bounding surface of the target structure. Scale selection is performed by a data driven entropy minimization approach. The manifold is analyzed for high intensity clusters, corresponding to protruding structures. Techniques involve EMclustering with automatic mode number estimation, directional statistics, and hierarchical clustering with a modified Bhattacharyya distance. The estimated number of high intensity clusters explicitly determines the type of pulmonary structures: nodule (0), attached nodule (1), vessel (2), junction (>3). We show accurate classification results for selected examples in thoracic CT scans. This local procedure is more flexible and efficient than
current state of the art and will help to improve the accuracy of general lung CAD systems.
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