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label fusion for segmentation via patch based on local weighted voting

Frontiers of Information Technology & Electronic Engineering 2017年第5期18卷 680-688页

作者： Kai ZHU Gang LIU Long ZHAO Wan ZHANG School of Automation Engineering Shanghai University of Electrical Power Athinoula A.Martinos Center for Biomedical Imaging Massachusetts General HospitalHarvard Medical School

label fusion is a powerful image segmentation strategy that is becoming increasingly popular in medical imaging. However, satisfying the requirements of higher accuracy and less running time is always a great challenge. In this paper we propose a novel patch-based segmentation method combining a local weighted voting strategy with Bayesian inference. Multiple atlases are registered to a target image by an advanced normalization tools(ANTs) algorithm. To obtain a segmentation of the target, labels of the atlas images are propagated to the target image. We first adopt intensity prior and label prior as two key metrics when implementing the local weighted voting scheme, and then compute the two priors at the patch level. Further, we analyze the label fusion procedure concerning the image background and take the image background as an isolated label when estimating the label prior. Finally, by taking the Dice score as a criterion to quantitatively assess the accuracy of segmentations, we compare the results with those of other methods, including joint fusion, majority voting, local weighted voting, majority voting based on patch, and the widely used Free Surfer whole-brain segmentation tool. It can be clearly seen that the proposed algorithm provides better results than the other methods. During the experiments, we make explorations about the influence of different parameters(including patch size, patch area, and the number of training subjects) on segmentation accuracy.

关键词： label fusion Local weighted voting Patch-based Background analysis

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AUTOMATIC SEGMENTATION OF HIPPOCAMPAL SUBFIELDS BASED ON MULTI-ATLAS IMAGE SEGMENTATION TECHNIQUES

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Journal of Electronics(China) 2014年第2期31卷 121-128页

作者： Shi Yonggang Zhang Xueping Liu Zhiwen Department of Electronic Engineering School of Information and ElectronicsBeijing Institute of Technology

The volume of hippocampal subfields is closely related with early diagnosis of Alzheimer's *** to the anatomical complexity of hippocampal subfields,automatic segmentation merely on the content of MR images is extremely *** presented a method which combines multi-atlas image segmentation with extreme learning machine based bias detection and correction technique to achieve a fully automatic segmentation of hippocampal *** diffeomorphic registration driven by symmetric mutual information energy was implemented in atlas registration,which allows multi-modal image registration and accelerates execution *** exponential function based label fusion strategy was proposed for the normalized similarity measure case in segmentation combination,which yields better combination *** test results show that this method is effective,especially for the larger subfields with an overlap of more than 80%,which is competitive with the current methods and is of potential clinical significance.

关键词： Hippocampal subfields Image segmentation Symmetric diffeomorphism Mutual information label fusion Extreme Learning Machine(ELM)

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