Open AccessSegmentation of breast MR images based on multiresolution level set algorithm
Author(s) -
Hong Fan,
Yuanxin Zhu,
Wang Fang-mei,
Xumei Zhang
Publication year - 2014
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.63.118701
Subject(s) - computer science , level set (data structures) , algorithm , segmentation , multiresolution analysis , artificial intelligence , wavelet , kernel (algebra) , image segmentation , boundary (topology) , scale (ratio) , scale space , computer vision , pattern recognition (psychology) , image (mathematics) , wavelet transform , image processing , mathematics , discrete wavelet transform , physics , mathematical analysis , combinatorics , quantum mechanics
This paper proposes a novel multiresolution level set algorithm to segment breast MR images, which have a large amount of information, intensity inhomogeneities, and weak boundary. The core of the algorithm is to get the coarse scale image by analyzing the image in multi-scale space with wavelet multiscale decomposition. Then, to segment the analysed results in terms of improved CV model. In order to deal with the effect of bias field on the global images, the algorithm introduces a local fitting term into the improved CV model and optimizes the coarse-scale segmentation result by using the Kernel function to further improve the CV model. Experimental results on both synthetic and real breast MR images demonstrate that the proposed algorithm can segment the images with intensity inhomogeneity effectively and efficiently, also it can segment the images far more accurately, computationally efficiently, and much less sensitively to the initial contour.