NDLI: ROI Boundary Detection Based on Geometric Active Contour Model in X-ray Skeletal Image

Content Provider	IEEE Xplore Digital Library
Author	Chuangxin Wang Zhong Yun Lie Yiyan Ye
Copyright Year	2008
Description	Author affiliation: Electr. & Inf. Eng. Coll., Changsha Univ. of Sci. & Technol., Changsha (Chuangxin Wang; Zhong Yun Lie)
Abstract	By utilizing the iterative solutions based on the level set method, the geometric active contour model (GACM) can be used to detect image boundaries and then solve the question of topology variation in curve evolution effectively, therefore, we adopt the GACM to investigate the boundary detection of digital X-ray skeletal images. According to the characteristics of medical images, we present an improved Chan-Vese method which combines regional information with image gradient information to increase the boundary detection capability. Theoretically speaking, the improved method can guarantee not only the speed and robustness of image division but also the accuracy of target extraction from the medical image background with abundant boundary layers. In practice, this method is proved to detect the boundaries of the epiphysis /metaphyseal regions of interest (EMROI) in X-ray skeletal images effectively and clearly.
Starting Page	228
Ending Page	232
File Size	495498
Page Count	5
File Format	PDF
ISBN	9780769531182
DOI	10.1109/BMEI.2008.231
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Solid modeling X-ray detectors Level set geometric active contour model (GACM) X-ray detection skeletal Topology X-ray imaging Active contours epiphysis /metaphyseal region of interest (EMROI) level set method Robustness Iterative methods Biomedical imaging
Content Type	Text
Resource Type	Article

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