24 December 2013 Breast cancer mitosis detection in histopathological images with spatial feature extraction
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Proceedings Volume 9067, Sixth International Conference on Machine Vision (ICMV 2013); 90670L (2013) https://doi.org/10.1117/12.2050050
Event: Sixth International Conference on Machine Vision (ICMV 13), 2013, London, United Kingdom
Abstract
In this work, cellular mitosis detection in histopathological images has been investigated. Mitosis detection is very expensive and time consuming process. Development of digital imaging in pathology has enabled reasonable and effective solution to this problem. Segmentation of digital images provides easier analysis of cell structures in histopathological data. To differentiate normal and mitotic cells in histopathological images, feature extraction step is very crucial step for the system accuracy. A mitotic cell has more distinctive textural dissimilarities than the other normal cells. Hence, it is important to incorporate spatial information in feature extraction or in post-processing steps. As a main part of this study, Haralick texture descriptor has been proposed with different spatial window sizes in RGB and La*b* color spaces. So, spatial dependencies of normal and mitotic cellular pixels can be evaluated within different pixel neighborhoods. Extracted features are compared with various sample sizes by Support Vector Machines using k-fold cross validation method. According to the represented results, it has been shown that separation accuracy on mitotic and non-mitotic cellular pixels gets better with the increasing size of spatial window.
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Abdülkadir Albayrak, Abdülkadir Albayrak, Gökhan Bilgin, Gökhan Bilgin, } "Breast cancer mitosis detection in histopathological images with spatial feature extraction", Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90670L (24 December 2013); doi: 10.1117/12.2050050; https://doi.org/10.1117/12.2050050
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