Mass Segmentation in Mammograms Based on the Combination of the Spiking Cortical Model (SCM) and the Improved CV Model
In this paper, a novel method based on CV model for the mass segmentation is proposed. Firstly, selecting the largest connected region, seeded region growing, and singular value decomposition (SVD) are used to pre-processing. After that apply the Spiking Cortical Model (SCM) on the pre-processed image to locate the lesion. Finally, the mass boundary is accurately segmented by the improved CV model. The validity of the proposed method is evaluated through two well-known digitized datasets (DDSM and MIAS). The performance of the method is evaluated with detection rate and area overlap. The results indicate the proposed scheme could obtain better performance when compared with several existing schemes.
This work was jointly supported by the National Natural Science Foundation of China (Grant No.61175012), Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No.20110211110026), the Fundamental Research Funds for the Central Universities of China (Grant No. lzujbky-2013-k06 & -lzujbky-2015-197) and the Central Universities of China under Grant lzujbky-2015-196.
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