期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2011
卷号：8
期号：5
出版社：IJCSI Press
摘要：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. 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. A new approach of feature selection is proposed which approximately reduces 60% of the features and association rule using image content is used for classification. The most interesting one is that oscillating search algorithm which is used for feature selection provides the best optimal features and no where it is applied or used for GLCM feature selection from mammogram. Experiments have been taken for a data set of 300 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 97.7% which is highly encouraging.
关键词：Mammogram; Gray Level Co-occurrence Matrix feature; Histogram Intensity; Genetic Algorithm; Branch and Bound technique; Association rule mining
