Segmentation method for medical image based on improved GrabCut

Segmentation of medical images has a lot of interferences because of the low contrast and fuzzy boundaries. It's hard to get perfect effect using present image segmentation methods, so we put forward an improved algorithm based on GrabCut and Gaussian mixture model (GMM) in this paper in order...

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Bibliographic Details
Published in:International journal of imaging systems and technology 2017-12, Vol.27 (4), p.383-390
Main Authors: Lu, Yu‐Wei, Jiang, Jian‐Guo, Qi, Mei‐Bin, Zhan, Shu, Yang, Jie
Format: Article
Language:English
Subjects:
Algorithms
figure of foreground
Gaussian Mixture model
GrabCut algorithm
Image contrast
Image segmentation
Medical imaging
Training
training set
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Summary:Segmentation of medical images has a lot of interferences because of the low contrast and fuzzy boundaries. It's hard to get perfect effect using present image segmentation methods, so we put forward an improved algorithm based on GrabCut and Gaussian mixture model (GMM) in this paper in order to obtain simplify interactive operation and better segmentation precision. We extend the GrabCut approach in 2 respects. Firstly, the initial GMMs of foreground and background were obtained by training sets, which could improve the algorithm's convergence rate. Secondly, the segmentation was restricted by the figure of foreground from training. Experimental results showed that compared with the traditional GrabCut algorithm, our proposed algorithm can simplify interactive operation (t = 14.33, P 
ISSN:0899-9457
1098-1098
DOI:10.1002/ima.22242