Rajashekar, K R and Sandeep, K J and Acharya, Aneesha K (2011) Mammogram Classification Using Texture Parameter Extraction and Rule Based Classifier. In: DRDO Sponsored Eighth Control Instrumentation System Conference, 2011, MIT Maniapl University, Manipal.

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Abstract

Digital mammography is one of the most effective technique that has demonstrated the ability to detect breast cancer at early stages with high sensitivity and specificity. It is very difficult for an radiologist to classify mammograms, due to textural variation in image intensity. This paper presents a novel approach for classification of mammograms into normal, benign or malignant. Here, statistical features of a mammogram are extracted using image processing and data mining techniques. Extracted features are fed to the petrained classifier. Based on features vector input, classifier classifies the mammograms into a particular category. The system has very high accuracy and has been verified with the ground truth given in the database (mini MIAS database).The false positive rate was as very low compared to the other existing methods

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