Association Technique based on Classification for Classifying Microcalcification and Mass in Mammogram

Currently, mammography is recognized as the most effective imaging modality for breast cancer screening. The challenge of using mammography is how to locate the area, which is indeed a solitary geographic abnormality. In mammography screening it is important to define the risk for women who have radiologically negative findings and for those who might develop malignancy later in life. Microcalcification and mass segmentation are used frequently as the first step in mammography screening. The main objective of this paper is to apply association technique based on classification algorithm to classify microcalcification and mass in mammogram. The system that we propose consists of: (i) a preprocessing phase to enhance the quality of the image and followed by segmentating region of interest; (ii) a phase for mining a transactional table; and (iii) a phase for organizing the resulted association rules in a classification model. This paper also illustrates how important the data cleaning phase in building the data mining process for image classification. The proposed method was evaluated using the mammogram data from Mammographic Image Analysis Society (MIAS). The MIAS data consist of 207 images of normal breast, 64 benign, and 51 malignant. 85 mammograms of MIAS data have mass, and 25 mammograms have microcalcification. The features of mean and Gray Level Co-occurrence Matrix homogeneity have been proved to be potential for discriminating microcalcification from mass. The accuracy obtained by this method is 83%.