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Proceedings Paper

Perceptual mass segmentation using eye-tracking and seed-growing
Author(s): Erting Ke; Wei Liu; Weidong Xu; Lihua Li; Bin Zheng; Juan Zhang; Lingnan Zhang
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Paper Abstract

In the paper, we propose a novel scheme for breast mass segmentation in mammography, which is based on visual perception and consists of two steps. Firstly, radiologists' eye-gazing data is recorded by the eye-tracker during reading and then clustered with a density-based spatial clustering of applications with noise (DBSCAN) algorithm to achieve seeds locating radiologists' regions of interest (ROIs). The seeds-based region growing (SBRG) algorithm is applied to buckle ROIs containing suspicious lesions. Secondly, in order to achieve fine lesion contour as final result, the ROIs are segmented with a multi-scale mass segmentation approach using active contour models. The result of applying the proposed method to the mammograms from both DDSM and Zhejiang Cancer Hospital shows that the achieved average of overlap rate is 0.5915 and the achieved average of misclassification rate is 0.6342. The innovative point of the proposed approach is to introduce visual perception into breast mass segmentation, which makes the result of mass segmentation meet radiologists' subjective demand.

Paper Details

Date Published: 23 February 2012
PDF: 8 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 831520 (23 February 2012); doi: 10.1117/12.912262
Show Author Affiliations
Erting Ke, Hangzhou Dianzi Univ. (China)
Wei Liu, Hangzhou Dianzi Univ. (China)
Weidong Xu, Hangzhou Dianzi Univ. (China)
Lihua Li, Hangzhou Dianzi Univ. (China)
Bin Zheng, Hangzhou Dianzi Univ. (China)
Univ. of Pittsburgh Medical Ctr. (United States)
Juan Zhang, Zhejiang Cancer Hospital (China)
Lingnan Zhang, Zhejiang Cancer Hospital (China)

Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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