Texture feature extraction using gray level statistical matrix for content-based mammogram retrieval

Abstract

Texture is one of the visual contents of an image used in content-based image retrieval (CBIR) to represent and index the image. Statistical textural representation methods characterize texture by the statistical distribution of the image intensity. This paper proposes a gray level statistical matrix from which four statistical texture features are estimated for the retrieval of mammograms from mammographic image analysis society (MIAS) database. The mammograms comprising architectural distortion, asymmetry, calcification, circumscribed, ill-defined, spiculated and normal classes are used in the experimentation. Precision, recall, retrieval rate, normalized average rank, average matching fraction, storage requirement and retrieval time are the performance measures used for the evaluation of retrieval performance. Using the proposed method, the highest mean precision rate obtained is 85.1 %. The results show that the proposed method outperforms the state-of-the-art texture feature extraction methods in mammogram retrieval problem.