Automatic brain tissue segmentation in MR images using hybrid atlas forest based on confidence‐weighted probability matrix. Issue 2 (22nd December 2018)

• Record Type: Journal Article
• Authors: Xu, Lijun; Liu, Hong; Song, Enmin; Jin, Renchao; Hung, Chih‐Cheng
• Is Part Of: International journal of imaging systems and technology. Volume 29:Issue 2(2019)
• Page Start: 97
• Page End: 109
• Publication Date: 2018-12-22
• Subjects: brain segmentation -- label fusion -- magnetic resonance imaging -- probability matrix -- random forest; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22301
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: The segmentation of specific tissues in an MR brain image for quantitative analysis can assist the disease diagnosis and medical research. Therefore, a robust and accurate method for automatic segmentation is necessary. Atlas‐based‐method is a common and effective method of automatic segmentation where an atlas refers to a pair of image consist of an intensity image and its corresponding label image. Apart from the general multi‐atlas‐based methods, which propagate labels through the single atlas then fuse them, we proposed a hybrid atlas forest based on confidence‐weighted probability matrix to consider the atlases set as a whole and treat each voxel differently. In the framework, we first register the atlas to the image space of target and calculate the confidence of voxels in the registered atlas. Then, a confidence‐weighted probability matrix is generated and it augments to the intensity image of the atlas or target for providing spatial information of the target tissue. Third, a hybrid atlas forest is trained to gather the features and correlation information among the atlases in the dataset. Finally, the segmentation of the target tissues is predicted by the trained hybrid atlas forest. The segment performance and the components efficiency of the proposed method are evaluated on the two public datasets. Based on the experiment results and quantitative comparisons, our method can gather spatial information and correlation among the atlases to obtain an accurate segmentation.