首页|A Chan-Vese Model Based on the Markov Chain for Unsupervised Medical Image Segmentation
A Chan-Vese Model Based on the Markov Chain for Unsupervised Medical Image Segmentation
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The accurate segmentation of medical images is crucial to medical care and research;however,many efficient supervised image segmentation methods require sufficient pixel level labels.Such requirement is difficult to meet in practice and even impossible in some cases,e.g.,rare Pathoma images.Inspired by traditional unsupervised methods,we propose a novel Chan-Vese model based on the Markov chain for unsupervised medical image segmentation.It combines local information brought by superpixels with the global difference between the target tissue and the background.Based on the Chan-Vese model,we utilize weight maps generated by the Markov chain to model and solve the segmentation problem iteratively using the min-cut algorithm at the superpixel level.Our method exploits abundant boundary and local region information in segmentation and thus can handle images with intensity inhomogeneity and object sparsity.In our method,users gain the power of fine-tuning parameters to achieve satisfactory results for each segmentation.By contrast,the result from deep learning based methods is rigid.The performance of our method is assessed by using four Computerized Tomography (CT) datasets.Experimental results show that the proposed method outperforms traditional unsupervised segmentation techniques.
medical imageunsupervised segmentationMarkov chain
Quanwei Huang、Yuezhi Zhou、Linmi Tao、Weikang Yu、Yaoxue Zhang、Li Huo、Zuoxiang He
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Key Laboratory of Pervasive Computing(Ministry of Education) and the Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China
School of Electronic and Information Engineering,Beihang University,Beijing 100191,China
Department of Nuclear Medicine,Peking Union Medical College Hospital,Beijing 100730,China
School of Clinical Medicine,Tsinghua University,and also with Beijing Tsinghua Changgung Hospital,Beijing 100084,China
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This paper was supported by the National Natural Science Foundation of ChinaThis paper was supported by the National Natural Science Foundation of Chinaand the Key-Area Research and Development Program of Guangdong Province
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