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《Biomedical Engineering and Clinical Medicine》 2013-02
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Cerebrovascular segmentation from magnetic resonance angiography based on Metropolis-SA algorithm

YANG Jun,ZHENG Qu-bo,WU Gui-liang,GAO Xing-wang,LI Hong-liang,ZHOU Shou-jun (No. 458th Hospital of PLA,Guangzhou 510602,Guangdong,China)  
Objective To study segmentation of brain magnetic resonance angiography by three-dimensional Markov random field(MRF) model.Methods Rayleigh and Gaussian mixture distributions were adopted to calculate the likelihood probability and the mixture parameters were accurately estimated by expectation maximization(EM) algorithm.Ising-MRF model was applied for the calculation of prior probability and the regularization parameter was estimated using trial-and-error method.To avoid the occurrence of local optimum solution during image segmentation with iterated condition mode(ICM),Metropolis-simulated annealing(MSA) based simulated annealing(SA) process were used in the current investigation.Results The global optimum solution were realized,and the proposed method can distinguish vessel as small as three voxels.The TOF-MRA data of Nanfang Hospital Imaging Center was used,which were collected from a 1.5 T GE MRI scanner with spatial resolution of 0.43 mm × 0.43 mm × 0.50 mm,raw data pixel size of 512 × 512 × 128 and actual size of 0.80 mm × 0.80 mm × 1.20 mm and 256 × 256 × 64.Each set of clinical data was compared using SA,ICM,MSA algorithm segmentation,and the results showed finite differences.The 15 iteration time consumption were 1 029 seconds,463 seconds and 560 seconds.Conclusion It is demonstrated that the segmentation results of three-dimension simulated data display smaller global error with the model.Meanwhile,the segmentation of real MRA data also demonstrates good effects in comparison with the maximum intensity projection.
【Fund】: 国家自然科学基金资助(61179020;31000450;60902103)
【CateGory Index】: TP391.41
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