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- Title
White Matter Segmentation Algorithm for DTI Images Based on Super-Pixel Full Convolutional Network.
- Authors
Mu, Yiping; Li, Qi; Zhang, Yang
- Abstract
Diffusion tensor imaging (DTI) is a new imaging method that can be used to non-invasively measure the diffusion coefficient of water molecules in biological tissue structures in recent years. Since the DTI data is a tensor space, its segmentation is different from ordinary MRI images. Based on the existing deep learning model, an improved image semantic segmentation method based on super-pixels and conditional random field is proposed. Firstly, this paper uses the existing feature extraction model based on deep learning to obtain rough semantic segmentation results, including high-level semantic information of the image but lacking details of the image. In addition, the super-pixel segmentation algorithm is implemented to obtain super-pixels that carries more low-level information. Secondly, due to the lack of image details in rough segmentation results, the segmentation of the edge of the image is inaccurate. In this paper, a boundary optimization algorithm is proposed to optimize the edge segmentation accuracy of the rough results. Finally, the use of super-pixels for local boundary optimization can improve the segmentation accuracy. Experiments results show that this segment is a practical and effective method.
- Subjects
ALGORITHMS; BRAIN; DIAGNOSTIC imaging; MAGNETIC resonance imaging; COMPUTERS in medicine; ARTIFICIAL neural networks; RESEARCH funding; QUALITATIVE research; QUANTITATIVE research; STATISTICAL models; WHITE matter (Nerve tissue)
- Publication
Journal of Medical Systems, 2019, Vol 43, Issue 9, pN.PAG
- ISSN
0148-5598
- Publication type
Article
- DOI
10.1007/s10916-019-1431-1