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A segmentation system based on clustering method for pediatric DTI images
Author(s) -
Liu Bin,
Jia Xianyong,
Jiang Qianfeng,
Huang Rui,
Zhang Hui,
Wan Chao
Publication year - 2015
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22126
Subject(s) - segmentation , artificial intelligence , diffusion mri , computer science , cluster analysis , computer vision , tractography , image segmentation , pattern recognition (psychology) , mean shift , white matter , fiber tract , scale space segmentation , magnetic resonance imaging , medicine , radiology
A system is presented for the segmentation of white matter fiber tracts in pediatric diffusion tensor magnetic resonance imaging (DTI) images. DTI is an in vivo method to delineate the connectivity of white matter fiber tracts in human brain by fiber tractography. Fiber tractography is a promising method to visualize the whole bundles of fiber tracts. Fiber tractography is unable to provide a quantitative analysis and description of specific white matter fiber tracts. Obviously, segmenting and clustering the fiber tracts into anatomical bundles play an important role in fiber tracts analysis. Traditional manual segmentation method requires neuroanatomical expertise and significant time. It can not be a standardized and widely used method for segmentation of complicated fiber tracts in pediatric DTI images. Hence, an image segmentation system with an adaptive mean shift (AMS) clustering method is proposed to cluster fiber tracts into bundles automatically in this article. In the image segmentation system, fiber similarity measure based on Euclidean distance is used in the clustering method. Since the increase of children's mental illness in recent years, segmentation of pediatric DTI images by clustering methods is focused in our research. The effectiveness and robustness of adaptive mean shift clustering algorithm for segmentation of fiber tracts are also evaluated by error analysis experiments. In addition, the experiment results show that adaptive mean shift method used in our system is more efficient and effective than K‐means and Fuzzy C‐means (FCM) clustering methods for the segmentation of fiber tracts in real pediatric DTI images. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 102–113, 2015