Detection of microcalcifications ROI in digital mammograms using two stages of neural networks

Yang-suk Lee; Seung-Chul Lim; Dong-Sun Park

doi:10.1117/12.441697

20 September 2001 Detection of microcalcifications ROI in digital mammograms using two stages of neural networks

Yang-suk Lee, Seung-Chul Lim, Dong-Sun Park

Proceedings Volume 4555, Neural Network and Distributed Processing; (2001) https://doi.org/10.1117/12.441697
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China

Abstract

In this paper, we present an efficient algorithm to detect microcalcifications ROI (Regions of Interest) in digital mammograms using two stages of neural networks. To efficiently detect microcalcifications ROI, we used four sequential processes; preprocessing for breast area detection, modified multilevel thresholding, ROI selection using simple thresholding filters and final ROI selection with two stages of neural networks. In modified multilevel thresholding, the shape property of microcalcification resulted from the gray-level difference with surroundings is used. This algorithm separates microcalcifications from tissues by applying the half-toning technique for different gray-levels. The first selection process with simple thresholding filters defines the filter parameters using the statistically extracted shape property and then it eliminates tissues, which are obviously recognized, to reduce the processing overhead in the next step. The final selection process using neural networks is to detect the ROI in two steps. Through the two stages of neural networks, ROIs with microcalcifications are selected. Each neural network compares and analyzes recognition performance after training. The ROI detection method for microcalcification used in this paper is the first stage for a CAD system. The designed ROI detection methods efficiently find 98.06% of with microcalcifications.

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Yang-suk Lee, Seung-Chul Lim, and Dong-Sun Park "Detection of microcalcifications ROI in digital mammograms using two stages of neural networks", Proc. SPIE 4555, Neural Network and Distributed Processing, (20 September 2001); https://doi.org/10.1117/12.441697

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