Automatic Location and Border Detection of the Left Ventricle in Magnetic Resonance Imaging
王上意、傅家啟
E-mail: 9315388@mail.dyu.edu.tw
ABSTRACT
The improvement of Magnetic Resonance Imaging technique and hardware had significantly improved the efficiency of the diagnosis on cardiac disease; however, the software technique still falls behind. In order to effectively analyze the large quantity of cardiac MRI’s, the computer aided diagnosis system should include the functions of automatic left ventricle locating and automatic left ventricle border detecting. In this thesis, the cross correlation based matching pursuit algorithm has been developed to
automatically locate the left ventricle. The performance of the proposed method is compared with convolution based matching pursuit algorithm. In addition, a canny-based GVF Snake algorithm has been developed to detect the endocardium and epicardium of left ventricle. The border detection performance is compared with the Dynamic Programming algorithm and conventional GVF Snake algorithm. Experimental results show that the proposed cross correlation based matching pursuit algorithm can reduce the CPU time of locating left ventricle. The detection performance of the Canny-based GVF Snake algorithm out performs the Dynamic Programming and the GVF Snake.
Keywords : Left Ventricle ; Magnetic Resonance Imaging ; Patten Feature Searching ; Border Detection ; Cross Correlation ; GVF Snake
Table of Contents
The improvement of Magnetic Resonance Imaging technique and hardware had significantly improved the efficiency of the diagnosis on cardiac disease; however, the software technique still falls behind. In order to effectively analyze the large quantity of cardiac MRI’s, the computer aided diagnosis system should include the functions of automatic left ventricle locating and automatic left ventricle border detecting. In this thesis, the cross correlation based matching pursuit algorithm has been developed to
automatically locate the left ventricle. The performance of the proposed method is compared with convolution based matching pursuit algorithm. In addition, a canny-based GVF Snake algorithm has been developed to detect the endocardium and epicardium of left ventricle. The border detection performance is compared with the Dynamic Programming algorithm and conventional GVF Snake algorithm. Experimental results show that the proposed cross correlation based matching pursuit algorithm can reduce the CPU time of locating left ventricle. The detection performance of the Canny-based GVF Snake algorithm out performs the Dynamic Programming and the GVF Snake.
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