Feature extraction and disease stage classification for glioma histopathology images

K. Fukuma, H. Kawanaka, V. B. S. Prasath, B. J. Aronow, and H. Takase

IEEE 17th International Conference on e-Health Networking, Applications and Services (Healthcom), pgs. 2, 2015

glioma, histopathology, features, biomedical

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This paper discusses the performance of feature descriptors for disease stage evaluation of Glioma images. In the field of histopathology, many evaluation methods for tissue images have been reported. However, pathologists have to analyze and evaluate many tissue images manually. In addition, the criteria of evaluation heavily depend on each pathologist’s experience and feelings. From this background, studies on computational pathology using computer vision have been reported. The proposed feature descriptors were, however, applied to specified diseases only, and we do not know whether these descriptors will be effective to other tissues or not. This paper applied the feature descriptors defined by previous studies to the Glioma images and investigated the effectiveness of them by using a statistical method. We also discussed a method to distinguish lowgrade from high-grade Glioma images by using the significant descriptors. After the experiments, more than 98% of Glioma images were classified correctly.