Share Email Print
cover

Proceedings Paper

Neural network for false positive reduction in full-field digital mammography with microcalcification shape preservation
Author(s): Dansheng Song; Wei Qian; Xuejun Sun; Jie Li; Baoyu Zheng; Robert A. Clark
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

We compared different edge detection algorithms and found that the 'Marr-Hildreth' edge detection have the best performance for macrocalcification shape preservation in our MCCs detection system. Edge detection is one of the most commonly used operations in image analysis. The edges form the outline of the macrocalcifications. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. This means that if the edges in an image can be identified accurately, all of the macrocalcifications can be located and the areas, perimeters, and shapes can be measured. So edge detection is one of the effect methods to preserve the shape of microcalcifications. Based on the edge enhancement method, a new mixed feature multistage method has been developed for improving the false positive (FP) reduction performance. Eleven features were extracted from both spatial and morphology domains in order to describe the micro-calcification clusters (MCCs) from different perspectives. These features are grouped into three categories: gray-level description, shape description and clusters description. This method was combined with neural network used in our false positive reduction, that reduce the false positive from 3.1/image to 0.1/image in 50 full field digital mammograms, The 50 mammograms are with 24 normal images and 26 abnormal images, including 41 microcalcification clusters in our database.

Paper Details

Date Published: 24 September 2001
PDF: 6 pages
Proc. SPIE 4554, Object Detection, Classification, and Tracking Technologies, (24 September 2001); doi: 10.1117/12.441641
Show Author Affiliations
Dansheng Song, Univ. of South Florida (United States)
Wei Qian, Univ. of South Florida (United States)
Xuejun Sun, Univ. of South Florida (United States)
Jie Li, Nanjing Univ. of Posts and Telecommunications (China)
Baoyu Zheng, Nanjing Univ. of Posts and Telecommunications (China)
Robert A. Clark, Univ. of South Florida (United States)


Published in SPIE Proceedings Vol. 4554:
Object Detection, Classification, and Tracking Technologies

© SPIE. Terms of Use
Back to Top