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Proceedings Paper

Application of neural-network-based multistage system for detection of microcalcification clusters in mammogram images
Author(s): Fleming Yuan Ming Lure; Roger S. Gaborski; Thaddeus F. Pawlicki
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Paper Abstract

A multi-stage system with image processing and artificial neural techniques is developed for detection of microcalcification in digital mammogram images. The system consists of (1) preprocessing stage employing box-rim filtering and global thresholding to enhance object-to- background contrast; (2) preliminary selection stage involving body-part identification, morphological erosion, connected component analysis, and suspect region segmentation to select potential microcalcification candidates; and (3) neural network-based pattern classification stage including feature map extraction, pattern recognition neural network processing, and decision-making neural network architecture for accurate determination of true and false positive microcalcification clusters. Microcalcification suspects are captured and stored in 32 by 32 image blocks, after the first two processing stages. A set of radially sampled pixel values is utilized as the feature map to train the neural nets in order to avoid lengthy training time as well as insufficient representation. The first pattern recognition network is trained to recognize true microcalcification and four categories of false positive regions whereas the second decision network is developed to reduce the detection of false positives, hence to increase the detection accuracy. Experimental results show that this system is able to identify true cluster at an accuracy of 93% with 2.9 false positive microcalcifications per image.

Paper Details

Date Published: 16 April 1996
PDF: 8 pages
Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); doi: 10.1117/12.237931
Show Author Affiliations
Fleming Yuan Ming Lure, Eastman Kodak Co. (United States)
Roger S. Gaborski, Eastman Kodak Co. (United States)
Thaddeus F. Pawlicki, Eastman Kodak Co. (United States)

Published in SPIE Proceedings Vol. 2710:
Medical Imaging 1996: Image Processing
Murray H. Loew; Kenneth M. Hanson, Editor(s)

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