Texture Analysis of Histopathological Images to Identify Anomalous Region

The pathological image segmentation is important in cancer diagnosis and grading. In human body, tissues are characterized with the organization of their components. Cancer causes the changes in these organization. In order to diagnose the cancer disease, pathologist visually examine the changes in the tissue. This examination mainly relies on the visual interpretation. It may lead to considerable amount of observer variability. Hence, they may or may not identify the abnormal tissue. To avoid this problem robust algorithms are introduced for segmentation. Graph Run Length Method (GRLM), Gray Level Co-occurrence Matrix (GLCM) provides efficient way to segment the abnormal tissue. To a pathological image color graph was automatically generated by using Graph Run Length Method (GRLM). Gray Level Co-occurrence Matrix (GLCM) provides texture features of pathological image. The graph provides the arrangement of cells and structure of cells in a tissue. Based on the arrangement of cells, structure of cells, GLCM based texture features we can segment the abnormal tissue efficiently.