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Mesothelioma is a form of cancer generally caused from previous exposure to asbestos. Although it was considered a rare neoplasm in the past, its incidence is increasing worldwide due to extensive use of asbestos. In the current practice of medicine, the gold standard for diagnosing mesothelioma is through a pleural biopsy with subsequent histologic examination of the tissue. The diagnostic tissue should demonstrate the invasion by the tumor and is obtained through thoracoscopy or open thoracotomy, both being highly invasive surgical operations. On the other hand, thoracocentesis, which is removal of effusion fluid from the pleural space, is a far less invasive procedure that can provide material for cytological examination. In this study, we aim at detecting and classifying malignant mesothelioma based on the nuclear chromatin distribution from digital images of mesothelial cells in effusion cytology specimens. Accordingly, a computerized method is developed to determine whether a set of nuclei belonging to a patient is benign or malignant. The quantification of chromatin distribution is performed by using the optimal transport-based linear embedding for segmented nuclei in combination with the modified Fisher discriminant analysis. Classification is then performed through a k-nearest neighborhood approach and a basic voting strategy. Our experiments on 34 different human cases result in 100% accurate predictions computed with blind cross validation. Experimental comparisons also show that the new method can significantly outperform standard numerical feature-type methods in terms of agreement with the clinical diagnosis gold standard. According to our results, we conclude that nuclear structure of mesothelial cells alone may contain enough information to separate malignant mesothelioma from benign mesothelial proliferations. © 2015 International Society for Advancement of Cytometry.

Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012. Thesis (Ph. D.) -- Bilkent University, 2012. Includes bibliographical references leaves 100-111. The histopathological examination of tissue specimens is essential for cancer diagnosis and grading. However, this examination is subject to a considerable amount of observer variability as it mainly relies on visual interpretation of pathologists. To alleviate this problem, it is very important to develop computational quantitative tools, for which image segmentation constitutes the core step. The segmentation algorithms in literature commonly use pixel-level color/texture descriptors that they define on image pixels for quantizing a tissue. On the other hand, it is usually harder to express domain specific knowledge about tissues, such as the spatial organization of tissue components, using only the pixel-level descriptors. This may become even harder for tissue images, which typically consist of a considerable amount of variation and noise at their pixel-level, such as similar color distribution of different tissue components, distortion in cell alignments, and color contrast caused by too much stain in a particular region. The previous segmentation algorithms are more susceptible to these problems as they work on pixel-level descriptors. In order to successfully address these issues, in this thesis, we introduce three new texture descriptors, namely ObjSEG, GraphRLM, and ObjCooc textures, and implement algorithms that use these descriptors for segmenting histopathological tissue images. We extract these texture descriptors on tissue components that are approximately represented by circular objects. Since these objectoriented texture descriptors are defined on the tissue components, and hence domain specific knowledge, they represent the spatial organization of the components better than their previous counterparts. Thus, our algorithms based on these descriptors give more effective and robust segmentation results. Furthermore, since the descriptors are not directly defined on image pixels, they are effective to alleviate the pixel-level problems. In our experiments, we tested our algorithms that use the proposed objectoriented descriptors on a dataset of 200 colon tissue images. Our experiments demonstrated that our new object-oriented feature descriptors led to high segmentation accuracies, also providing a reasonable number of segmented regions. Compared with its previous counterparts, the experimental results also showed that our proposed algorithms are more effective in segmenting histopathological images. Tosun, Akif Burak Ph.D.