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Proceedings Paper

Computer-aided detection of microcalcifications in digital mammograms using a synthetic technique
Author(s): Ruiping Wang; Baikun Wan; Zhenhe Ma; Xuchen Cao
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Paper Abstract

Clustered microcalcifications (MCCs) on mammograms are important hints of breast cancer. Nevertheless, it is a complex and difficult task for radiologists to detect the clustered MCCs from the tissue background of mammograms only by naked eyes. This paper presents a method for computer-aided detection of MCCs in digital mammograms. The detection algorithm mainly consists of two different methods. The first one, based on the difference-image technique, recognizes high-frequency signals and very high-frequency noise. The second one is able to extract high-frequency signal by exploiting a wavelet based noise suppression and neural network (NN) classification. In the false-positive reduction step, false signals are separated from MCCs by means of an AND operation on signals from two methods. The algorithm is tested with a series of clinical mammograms. A sensitivity ofmore than 90% is obtained at a relatively low false-positive (FP) detection of 2.18 per image. The results are compared with thejudgement ofradiological experts, and they are very encouraging.

Paper Details

Date Published: 31 July 2002
PDF: 6 pages
Proc. SPIE 4875, Second International Conference on Image and Graphics, (31 July 2002); doi: 10.1117/12.477210
Show Author Affiliations
Ruiping Wang, Tianjin Univ. (China)
Baikun Wan, Tianjin Univ. (China)
Zhenhe Ma, Tianjin Univ. (China)
Xuchen Cao, Tianjin Medical Univ. Tumor Hospital (China)

Published in SPIE Proceedings Vol. 4875:
Second International Conference on Image and Graphics
Wei Sui, Editor(s)

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