Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field

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• 作者：Jingxin Nie ; Zhong Xue ; Tianming Liu ; Geoffrey S. Young ; Kian Setayesh ; Lei Guo ; Stephen T.C. Wong
• 关键词：MRI ; Segmentation ; Brain tumor
• 刊名：Computerized Medical Imaging and Graphics
• 出版年：2009
• 出版时间：September 2009
• 年：2009
• 卷：33
• 期：6
• 页码：431-441
• 全文大小：1878 K

文摘

A variety of algorithms have been proposed for brain tumor segmentation from multi-channel sequences, however, most of them require isotropic or pseudo-isotropic resolution of the MR images. Although co-registration and interpolation of low-resolution sequences, such as T2-weighted images, onto the space of the high-resolution image, such as T1-weighted image, can be performed prior to the segmentation, the results are usually limited by partial volume effects due to interpolation of low-resolution images. To improve the quality of tumor segmentation in clinical applications where low-resolution sequences are commonly used together with high-resolution images, we propose the algorithm based on Spatial accuracy-weighted Hidden Markov random field and Expectation maximization (SHE) approach for both automated tumor and enhanced-tumor segmentation. SHE incorporates the spatial interpolation accuracy of low-resolution images into the optimization procedure of the Hidden Markov Random Field (HMRF) to segment tumor using multi-channel MR images with different resolutions, e.g., high-resolution T1-weighted and low-resolution T2-weighted images. In experiments, we evaluated this algorithm using a set of simulated multi-channel brain MR images with known ground-truth tissue segmentation and also applied it to a dataset of MR images obtained during clinical trials of brain tumor chemotherapy. The results show that more accurate tumor segmentation results can be obtained by comparing with conventional multi-channel segmentation algorithms.