Texture Features for the Detection of Acute Lymphoblastic Leukemia

Acute Lymphoblastic Leukemia (ALL) is a cancer of the blood or bone marrow. Detection of ALL is usually done by skilled pathologists, automatic detection of leukemia will reduce the diagnosis time and will also be independent of the skills of the pathologist. In this paper, we propose using texture descriptors extracted from the nucleus image for detection of ALL. The disease causes change in the chromatin distribution of the nucleus, which can be observed in the form of texture. We have used two texture features, namely Local Binary pattern and Gray Level Co-occurrence Matrix for automatic detection of ALL. A comparative analysis of both the features is presented. It is seen that LBP features perform better than GLCM features.