NDLI: Extracting hidden visual information from mammography images using conjugate image enhancement software

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Extracting hidden visual information from mammography images using conjugate image enhancement software

Content Provider	IEEE Xplore Digital Library
Author	Zhiwen Yan Yan Zhang Bing Liu Jeffrey Zheng Lian Lu Yingfu Xie Zhi Liang Jing Li
Copyright Year	2005
Description	Author affiliation: Sch. of Software, Yunnan Univ., Kunming, China (Zhiwen Yan; Yan Zhang; Bing Liu; Jeffrey Zheng)
Abstract	Most early breast cancers can be diagnosed by detecting calcification clusters in mammography X-ray images. The clusters appear as groups of small, bright particles with arbitrary shapes. Detecting micro-calcifications is difficult because they are embedded in a non-homogeneous background. Many missed radiology diagnoses can be attributed to human factors such as the use of subjective criteria or variable criteria in decision making, distraction by other image features, the large number of images to be inspected, or just simple oversight. Consequently there are very good reasons for pursuing reliable and effective methods for micro-calcifications detection. While many methods for micro-calcification segmentation have been developed in the past ten years, they either require manual threshold adjustments or depend on local statistics to compute those thresholds. This paper presents a new fully automated, parameter-free, and local statistics independent, algorithm for micro-calcification segmentation in mammography X-ray images.
File Size	1335579
File Format	PDF
ISBN	0780393031
DOI	10.1109/ICIA.2005.1635092
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation X-ray detectors X-ray detection Cancer detection Mammography Breast cancer Data mining Statistics Image enhancement X-ray imaging
Content Type	Text
Resource Type	Article

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