Brain Image Segmentation Techniques
A deep study about tumor
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In relation to the tumor location detection, a novel method is presented based on wavelet packet transform. It can be employed in several MR image models which show tumor in hyper intensity. In relation to tumor extraction, two strategic methods are proposed. One uses Genetic K-Means algorithm. A strategic plan is made in introducing a new error based fitness function in genetic algorithm. It performs remarkably in images which contain non-continuous tumor regions. The Second method uses region based active contour model. It adapts a strategic plan to restrict the energy of the contour. The re...
In relation to the tumor location detection, a novel method is presented based on wavelet packet transform. It can be employed in several MR image models which show tumor in hyper intensity. In relation to tumor extraction, two strategic methods are proposed. One uses Genetic K-Means algorithm. A strategic plan is made in introducing a new error based fitness function in genetic algorithm. It performs remarkably in images which contain non-continuous tumor regions. The Second method uses region based active contour model. It adapts a strategic plan to restrict the energy of the contour. The results show that the proposed method outperforms some existing methods which handled the multimodal brain tumor segmentation (MICCAI) challenges. A region adjacency graph (RAG) and a novel technique of grafting which mimics the grafting process in horticulture and statistical rules utilized to segment the tumor components.
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