Automatic detection of abnormal mammograms in mammographic images

This paper proposes a detection method for abnormal mammograms in mammographic datasets based on the novel abnormality detection classifier (ADC) by extracting a few of discriminative features, first-order statistical intensities and gradients. As tumorous masses are often indistinguishable from the surrounding parenchyma, automatic mass detection on highly complex breast tissues has been a challenge. However, most tumor detection methods require extraction of a large number of textural features for further multiple computations. The study first investigates image preprocessing techniques for obtaining more accurate breast segmentation prior to mass detection, including global equalization transformation, denoising, binarization, breast orientation determination and the pectoral muscle suppression. After performing gray level quantization on the breast images segmented, the presented feature difference matrices could be created by five features extracted from a suspicious region of interest (ROI); subsequently, principal component analysis (PCA) is applied to aid the determination of feature weights. The experimental results show that applying the algorithm of ADC accompanied with the feature weight adjustments to detect abnormal mammograms has yielded prominent sensitivities of 88% and 86% on the two respective datasets. Comparing other automated mass detection systems, this study proposes a new method for fully developing a high-performance, computer-aided decision (CAD) system that can automatically detect abnormal mammograms in screening programs, especially when an entire database is tested.