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Pathological image analysis plays a significant role in effective disease diagnostics. Quantitative microscopy has supplemented clinicians with accurate results for diagnosis of dreaded diseases such as leukemia, hepatitis, AIDS, psoriasis. In this paper we present a texture based approach for automated leukemia detection. Acute lymphocytic leukemia (ALL) is a malignant disease characterized by the accumulation of lymphoblast in the bone marrow. Texture features of the blood nucleus are investigated for diagnostic prediction of ALL. Other shape features are also extracted to classify a lymphocytic cell in the blood image into normal lymphocyte or lymphoblast (blasts). Initial segmentation is done using K-means clustering which segregates leukocytes or white blood cells (WBC) from other blood components i.e. erythrocytes and platelets. The results of K-means are used for evaluating individual cell shape, texture and other features for final detection of leukemia. A total of 108 blood smear images were considered for feature extraction and final performance evaluation is validated with the results of a hematologist.

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Filariasis is a global health hazard. Incidence of fi lariasis is remarkably high in Indian subcontinent. Apart from typical cases it is not common to fi nd microfi laria and adult worm in tissue section and needle aspiration of the breast lump. Presence of microfi laria and adult worm in the breast is a rare fi nding. We report a case of fi larial nodule in the breast of a 40-year-old woman who presented with a painless mass in the breast since last 1 month. It was diagnosed as fi broadenoma breast. Fine needle aspiration cytology showed mainly chronic infl ammatory cells. The nodule was excised in toto and processed for histopathological examination. Histopathology confi rmed the adult worm with granuloma in the breast nodule.