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This study proposes and appraise a gray level cooccurrence matrix (GLCM) for extracting the feature of cell regions in microscopic image into four region types: positive cancer cell, negative cancer cell, lymphocyte and stromal cell. The classification task uses decision tree with cross validation. To give a high classification performance, the main focus of interest is feature extraction task. Twenty-two texture features of GLCM have used to analysis images at four directions and six scales of gray-level quantization. A set of these texture features is used in 2045 images for training and testing. The result shows that the classification accuracy obtained from decision tree is 95.21%. It is demonstrated that the proposed GLCM texture features and decision tree can classify the histological structures in microscopic image and can be applied to improve and to develop an accurate cell counting of computer-aided diagnosis system for breast cancer prognosis. Index Terms-Gray-level co-occurrence matrix, texture features, breast cancer, estrogen, immunohistochemistry, microscopic image

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