Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field.
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.
Nie
Jingxin
J
Methodist Center for Biotechnology and Informatics, The Methodist Hospital Research Institute, Weill Cornell Medical College, USA.
Xue
Zhong
Z
Liu
Tianming
T
Young
Geoffrey S
GS
Setayesh
Kian
K
Guo
Lei
L
Wong
Stephen T C
ST
D000465:Algorithms; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000592:standards*; D008390:Markov Chains; D010363:Pattern Recognition, Automated / Q000379:methods*