Improving the sub-cortical gm segmentation using evolutionary hierarchical region merging

Çiftçioğlu, Mustafa Ulaş
Segmentation of sub-cortical Gray Matter (GM) structures in magnetic resonance brain images is crucial in clinic and research for many purposes such as early diagnosis of neurological diseases, guidance of surgical operations and longitudinal volumetric studies. Unfortunately, the algorithms that segment the brain into 3 tissues usually suffer from poor performance in the sub-cortical region. In order to increase the detection of sub-cortical GM structures, an evolutionary hierarchical region merging approach, abbreviated as EHRM, is proposed in this study. Through EHRM, an intensity based region merging is utilized while merging is allowed to proceed among disconnected regions. Texture information is also incorporated into the scheme to prevent the region merging between tissues with similar intensity but different texture properties. The proposed algorithm is tested on real and simulated datasets. The performance is compared with a popular segmentation algorithm, which is also intensity driven: the FAST algorithm [1] in the widely used FSL suite. EHRM is shown to make a significant improvement the detection of sub-cortical GM structures. Average improvements of 10%, 36% and 22% are achieved for caudate, putamen and thalamus respectively. The accuracy of volumetric estimations also increased for GM and WM. Performance of EHRM is robust in presence of bias field. In addition, EHRM operates in O(N) complexity. Furthermore, the algorithm proposed here is simple, because it does not incorporate spatial priors such as an atlas image or intensity priors. With these features, EHRM may become a favorable alternative to the existing brain segmentation tools.


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Citation Formats
M. U. Çiftçioğlu, “Improving the sub-cortical gm segmentation using evolutionary hierarchical region merging,” M.S. - Master of Science, Middle East Technical University, 2011.