Image Mining for Mammogram Classification to detect breast cancer by Association Reverse Rule Using Statistical and GLCM features

The image mining technique deals with the extraction of implicit knowledge and image with data relationship or other patterns not explicitly stored in the images. It is an extension of data mining to image domain. The main objective of this paper is to apply image mining in the domain such as breast mammograms to classify and detect the cancerous tissue. Mammogram image can be classified into normal, benign and malignant class and to explore the feasibility of data mining approach. Results will show that there is promise in image mining based on content. It is well known that data mining techniques are more suitable to larger databases than the one used for these preliminary tests. In particular, a Computer aided method based on association rules becomes more accurate with a larger dataset. Traditional association rule algorithms adopt an iterative method to discovery frequent item set, which requires very large calculations and a complicated transaction process. Because of this, a new association rule algorithm is proposed in this paper. Experimental results show that this new method can quickly discover frequent item sets and effectively mine potential association rules. A total of 26 features including histogram intensity features and GLCM features are extracted from mammogram images. Experiments have been taken for a data set of 322 images taken from MIAS of different types with the aim of improving the accuracy by generating minimum no. of rules to cover more patterns. The accuracy obtained by this method is approximately 97% which is highly encouraging.