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Proceedings Paper

Detection of microcalcifications ROI in digital mammograms using two stages of neural networks
Author(s): Yang-suk Lee; Seung-Chul Lim; Dong-Sun Park
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Paper 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.

Paper Details

Date Published: 20 September 2001
PDF: 7 pages
Proc. SPIE 4555, Neural Network and Distributed Processing, (20 September 2001); doi: 10.1117/12.441697
Show Author Affiliations
Yang-suk Lee, Chonbuk National Univ. (South Korea)
Seung-Chul Lim, Woosong Technical College (South Korea)
Dong-Sun Park, Chonbuk National Univ. (South Korea)

Published in SPIE Proceedings Vol. 4555:
Neural Network and Distributed Processing
Xubang Shen; Jianguo Liu, Editor(s)

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