Active Contour Model for Ultrasound Images with Rayleigh Distribution
Author(s) -
Guodong Wang,
Qian Dong,
Zhenkuan Pan,
Ximei Zhao,
Jinbao Yang,
Cunliang Liu
Publication year - 2014
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2014/295320
Subject(s) - speckle noise , multiplicative noise , rayleigh distribution , segmentation , artificial intelligence , speckle pattern , computer science , active contour model , computer vision , image segmentation , multiplicative function , noise (video) , pattern recognition (psychology) , rayleigh scattering , mathematics , image (mathematics) , optics , physics , mathematical analysis , signal transfer function , digital signal processing , analog signal , computer hardware
Ultrasound images are often corrupted by multiplicative noises with Rayleigh distribution. The noises are strong and often called speckle noise, so segmentation is a hard work with this kind of noises. In this paper, we incorporate multiplicative noise removing model into active contour model for ultrasound images segmentation. To model gray level behavior of ultrasound images, the classic Rayleigh probability distribution is considered. Our model can segment the noisy ultrasound images very well. Finally, a fast method called Split-Bregman method is used for the easy implementation of segmentation. Experiments on a variety of synthetic and real ultrasound images validate the performance of our method
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