Abstract
Microcalcifications (MCs) are the main symptoms of breast cancer in the mammograms. This paper proposed a new computer-aided diagnosis (CAD) algorithm to detect the MCs. At first, discrete wavelet transform (DWT) was applied to extract the high-frequency signal, and thresholding with hysteresis was used to locate the suspicious MCs. Then, filling dilation was utilized to segment the desired regions. During the detection process, ANFIS was applied for auto-adjustment, making the CAD more adaptive. Finally, the segmented MCs were classified with MLP, and a satisfying result validated this method.
Supported by Nature Science Foundation of China (No. 60272029) and Nature Science Foundation of Zhejiang Province of China (No. M603227).
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© 2006 Springer-Verlag Berlin Heidelberg
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Xu, W., Zhang, Z., Xia, S., Duan, H. (2006). Detection of Microcalcifications Using Wavelet-Based Thresholding and Filling Dilation. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing in Signal Processing and Pattern Recognition. Lecture Notes in Control and Information Sciences, vol 345. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-37258-5_95
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DOI: https://doi.org/10.1007/978-3-540-37258-5_95
Publisher Name: Springer, Berlin, Heidelberg
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