Adaptive Distance Metric Learning for Diffusion Tensor Image Segmentation
Publication in refereed journal


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摘要High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.
著者Kong YY, Wang DF, Shi L, Hui SCN, Chu WCW
期刊名稱PLoS ONE
出版年份2014
月份3
日期20
卷號9
期次3
出版社PUBLIC LIBRARY SCIENCE
國際標準期刊號1932-6203
語言英式英語
Web of Science 學科類別Multidisciplinary Sciences; Science & Technology - Other Topics

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