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It establishes two mass models to characterize the various masses, and the ones in the denser tissue are represented with Model I, while the ones in the fatty tissue are represented with Model II. In order to improve the performance of mass segmentation on mammograms, an intelligent algorithm is proposed in this paper. And the final classification precision of the focuses is: for 120 mammograms of 60 cases, the true positive rate of the masses and the MCs are respectively 93.6% and 96.9%, and the false positive per image of them are 0.63 and 0.2. The experiments show that CMAC has very different performance for the training set and the testing set., ANFIS only performs well when the dimensionality of the features of the samples is comparatively low, while MLP is the most stable classifier, especially when the dimensionality of the features increases and the number of the samples is limited. In order to choose an effective classifier, three neural networks are used to identify the focuses respectively, and their performance is compared and then discussed.

And the identification on whether a focus is positive or negative is presented in this paper. In the previous research, we had studied the detection algorithm of the microcalcifications (MCs) and the masses. Mammography is regarded as the most reliable detection method of breast tumor and with the increase of cases, computer-aided diagnosis/detection (CAD) has been widely studied and applied to assist the radiologists. The incidence of breast cancer has been quickly raised in China recently. And the proposed CAD system had a totally better performance than the conventional methods in the experiments. Finally, three neural networks were used to classify the lesions for comparison, and MLP was confirmed as the most stable one. Then, it utilized a model-based detection algorithm for the masses, using different information extraction ways to detect different kinds of masses, and applied a filling dilation restricted with Canny edge detector and energy field to segment the masses. Secondly, it used a thresholding with hysteresis in the wavelet domain to locate the microcalcifications (MCs), and applied an ANFIS-based filling dilation algorithm to extract the MCs.

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At first, it used a segmentation threshold optimization method for the pectoral muscle by applying a series of regions of interest (ROIs) with various sizes, presented a zonal Hough transform to segment the rough region, and then applied polygon fitting to approach the edge of the pectoral muscle.

In order to assist the radiologists in reading the CR mammograms, a novel computer-aided detection (CAD) system was presented in this paper. Breast cancer is one of the most dangerous tumors for middle-aged and older women in China, and CR mammography is its most reliable early detection method in the clinic.

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