Left Ventricular Wall Detection from MRI Scans using Random Walk
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The left ventricle (LV) is one of four chambers (two atria and two ventricles) in the human heart. Because it supplies oxygenated blood to the entire body, it possesses a stance of great significance in the medical diagnosis. In order to know the problem against any chest pain, physicians first try to look to the shape of the LV. Therefore, its segmentation always remains as the first and foremost step. The accuracy of any segmentation is usually achieved through lessening the user interaction. A semi-automatic method for image segmentation that uses random walks was introduced in 2006. This approach requires user guidance to define the desired content to be extracted in the image. This is fast, effective and intuitive which can successfully perform segmentation irrespective of the image type. At the same time, its performance depends largely on the degree of homogeneity and separability of the objects present in the image. The lesser is the obscurity, unlike the cardiac magnetic resonance images, the better is its execution. Since myocardium (thick layer of cardiac muscle) is responsible for the contraction and relaxation of the ventricle, its inner (endocardium) and outer (epicardium) lining should be extracted simultaneously. The main contributions of this thesis are as follows: 1) In endocardial wall detection, Random Walk approach faces many challenges while applying this algorithm on ischemic cardiac magnetic resonance (CMR) images (as they are more obscure), one of them is initial seed(s) selection. Furthermore, the free parameter D in this algorithm does have the influence on its performance, which is usually decided by the user. In order to reduce the user interaction (because more user interaction introduces larger variability in the performance), attempts have been made to solve these two issues in our research and made the algorithm automatic, 2) The grey level distribution of myocardial muscle does not differ much from the outer surrounding muscles, therefore, Random Walk approach is little suitable for its meaningful segmentation. Instead, a modified active contour model that operates on the endocardial boundary is applied to section out the epicardium, 3) The methodologies adapted in endocardial wall detection do not produce satisfactory result, especially in case of short axis CMR images. After a thorough review of the nature of weighting function, three functions for the same purpose have been suggested to achieve better segmentations, and finally 4) To compare the performances of these weighting functions, an unsupervised technique has been proposed in the end..
Supervisor: J. S.. Sahambi
ELECTRONICS AND ELECTRICAL ENGINEERING