Abstract

Mammography is a widely used screening tool and is the gold standard for the early detection of breast cancer. The classification of breast masses into the benign and malignant categories is an important problem in the area of computer-aided diagnosis of breast cancer. Content-Based Image Retrieval (CBIR) allows the retrieval of similar images based on features extracted directly from image data. Our purpose in this paper is to develop a method of image mammogram feature extraction (microcalcifications and masses features) in CBIR system. The image features we investigate in this paper are a set of combinations of geometric image moments which are invariant to translation, scale, rotation and contrast. Performance evaluation was done using precision and recall curves obtained from comparison between the query and retrieved images.
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Keywords: Digital Mammogram, Texture Features, Geometric Moments, Content-Based Image Retrieval.

 

(P1150930860, 0.5 MB)