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Proceedings Paper

Method of automatic detection of tumors in mammogram
Author(s): Mei Xie; Zheng Ma
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Paper Abstract

Prevention and early diagnosis of tumors in mammogram are foremost. Unfortunately, these images are often corrupted by the noise due to the film noise and the background texture of the images, which did not allow isolation of the target information from the background noise, and often results in the suspicious area to be analyzed inaccurately. In order to achieve more accurate detection and segmentation tumors, the quality of the images need to improve, (including to suppressing noise and enhancing the contrast of the image). This paper presents a new adaptive histogram threshold method approach for segmentation of suspicious mass regions in digitized images. The method use multi-scale wavelet decomposition and a threshold selection criterion based on a transformed image¡¯s histogram. This separation can help eliminate background noise and discriminates against objects of different size and shape. The tumors are extracted by used an adaptively bayesian classifier. We demonstrate that the method proposed can greatly improve the accuracy of detection in tumors.

Paper Details

Date Published: 18 September 2001
PDF: 9 pages
Proc. SPIE 4556, Data Mining and Applications, (18 September 2001); doi: 10.1117/12.440290
Show Author Affiliations
Mei Xie, Univ. of Electronic Science and Technology of China (China)
Zheng Ma, Univ. of Electronic Science and Technology of China (China)

Published in SPIE Proceedings Vol. 4556:
Data Mining and Applications
Deren Li; Jie Yang; Jufu Feng; Shen Wei, Editor(s)

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