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Informatics in Radiology

Automatic and Adaptive Brain Morphometry on MR Images¹

¹ From the Shenzhen Institute of Advanced Integration Technology, Chinese Academy of Sciences, Chinese University of Hong Kong, 3A, Nanshan Medical Instruments Park, 1019 Nanhai Ave, Shenzhen 518067, China (Q.H.); and Biomedical Imaging Lab, Singapore Bio-imaging Consortium, Agency for Science, Technology and Research, Singapore (G.Q., M.T., S.H.). Presented as an Informatics exhibit at the 2006 RSNA Annual Meeting. Received April 25, 2007; revision requested June 22 and received July 24; accepted August 14. All authors have no financial relationships to disclose. Address correspondence to Q.H. (e-mail: qm.hu{at}siat.ac.cn).

Automatic segmentation of brain tissue on magnetic resonance images remains a challenge due to variations in brain shape and size, use of different pulse sequences, overlapping signal intensities, and imaging artifacts. An image analysis system that combines robust image processing techniques with anatomic knowledge was developed to meet this challenge. The system is fast, accurate, and robust to the variability of brain anatomy and imaging conditions and is useful for studying the brain in healthy adults, patients with a shrunken brain due to brain atrophy, and children. With this new thresholding method, the range of the proportion of brain tissue can be determined, thereby making good segmentation possible even in the presence of intrasectional inhomogeneity and noise. The system can adaptively adjust the morphologic processing to break the connection between brain and nonbrain tissue while preserving small brain fragments. It can also segment the white matter and gray matter of the two hemispheres separated by the midsagittal plane. The segmentation results can be visualized in either two or three dimensions. The system has been validated against 53 public data sets and qualitatively tested on 47 clinical data sets, yielding a better accuracy than that of the four most popular methods of brain segmentation.

© RSNA, 2008