Left Ventricle MRI Image Segmentation by Unifying Statistic Model and Curves Evolving
ZHOU ZeMing1,2,CHEN Qiang2, Pheng Ann Heng3 , XIA DeShen2
1.Meteorology College, PLA University of Science and Technology, Nanjing 211101 2.Department of Computer, Nanjing University of Science and Technology, Nanjing 210094 3.Department of Computer Science and Engineering, Chinese University of HongKong, HongKong
Abstract An MRI image segmentation algorithm is proposed by unifying region statistic model and image gradient information. Due to cardiac deformation and blood flowing, weak edges, local gradient maximum regions and artifacts often can be found in the MRI images. The level set method which constructs stopping term with image gradient intensity cannot segment those cardiac MRI images accurately. A twostage algorithm is thus proposed to address the difficulties. Firstly, incorporating prior knowledge about the cardiac MRI and the image histogram, the populations of pixels are given. The prior probabilities of those classes and the parameters of the Gaussian distributions are estimated with Maximumlikelihood principle. With the posterior probability of pixel belonging to ROI, the velocity function of level set is constructed to search for the rough boundary of ROI. Next, another velocity function based on the gradient vector flow is designed to locate the edges accurately. The experimental results demonstrate the effectiveness of the segmentation algorithm.
ZHOU ZeMing,CHEN Qiang,Pheng Ann Heng等. Left Ventricle MRI Image Segmentation by Unifying Statistic Model and Curves Evolving[J]. , 2006, 19(4): 509-514.
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2006, Vol. 19 
