Open AccessActive contour driven by local divergence energies for ultrasound image segmentation
Author(s) -
Yuan Jianjun
Publication year - 2013
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2012.0461
Subject(s) - level set (data structures) , active contour model , image segmentation , divergence (linguistics) , level set method , segmentation , noise (video) , artificial intelligence , energy functional , computer science , computer vision , computation , image (mathematics) , pattern recognition (psychology) , function (biology) , mathematics , algorithm , mathematical analysis , linguistics , philosophy , evolutionary biology , biology
In this study, a new local region‐based active contour model in a variational level set formulation for ultrasound image segmentation is proposed. The energy function is formulated based on the local divergence with likelihood ratio. The proposed model can handle blurry boundaries and noise problems. In addition, the regularity of the level set function is intrinsically preserved by the level set regularisation term to ensure accurate computation. The authors only adopt a level set function to define the partition of image domain into two disjoined regions. Experimental results demonstrate desirable performance of the authors’ method for synthetic images with different level noise and ultrasound images with weak boundaries and noise.