NDLI: A Graph-Based Segmentation Method for Breast Tumors in Ultrasound Images

Content Provider	IEEE Xplore Digital Library
Author	Suying Lee Qinghua Huang Lianwen Jin Minhua Lu Tianfu Wang
Copyright Year	2010
Abstract	This paper introduces a graph-based image segmentation method for detecting breast tumors in ultrasound images. The proposed segmentation algorithm based on the minimum spanning trees in a graph generated from an image, can automatically detect tumor regions and segment lesions in ultrasound images. The algorithm for segmenting breast ultrasound images consists of 3 steps, i.e. the nonlinear coherent diffusion model for speckle reduction, the graph construction for mapping the image to a graph, and the mergence of smaller regions. A pairwise region comparison predicate comparing the inter-component differences with the within component differences, is used to determine whether or not two regions should be merged. Experimental results have shown that the proposed segmentation algorithm is simply structured, robust to noises, highly efficient and much flexible in comparison with Fuzzy C means clustering. It can successfully detect tumors and extract lesions in ultrasound images more accurately. We hope that our method could be useful in various medical practices, providing an alternative way for ultrasound image analysis.
Starting Page	1
Ending Page	4
File Size	495488
Page Count	4
File Format	PDF
ISBN	9781424447121
ISSN	21517622
DOI	10.1109/ICBBE.2010.5517619
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Breast tumors Ultrasonic imaging Tree graphs Clustering algorithms Image generation Speckle Breast neoplasms Noise robustness Lesions
Content Type	Text
Resource Type	Article

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