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• W207905448 abstract "The major goal of this dissertation is to present a new clustering algorithm using information theoretic measures and apply the algorithm to segment Magnetic Resonance (MR) Images. Since MR images are highly variable from subject to subject, data driven segmentation methods seem appropriate. We developed a new clustering evaluation function based on information theory that outperforms previous clustering algorithms, and the new cost function works as a valley seeking algorithm. Since optimization of the clustering evaluation function is difficult because of its stepwise nature and existence of local minima, we developed an improvement on the K-change algorithm used commonly in clustering problems. When applied to nonlinearly separable data, the algorithm performed with very good results, and was able to find the nonlinear boundaries between clusters without supervision. The clustering algorithm is applied to segment brain MR images with successful results. A feature set is created from MR images using entropy measures of small blocks from the input image. Clustering the whole brain image is computationally intensive. Therefore, a small section of the brain is first used to train the clustering algorithm. Afterwards, the rest of the brain is clustered using the results obtained from the training image by using the distance measure proposed. The algorithm is easy to apply and the calculations are simplified by choosing a proper distance measure which does not require numerical integration." @default.
• W207905448 created "2016-06-24" @default.
• W207905448 creator A5019504861 @default.
• W207905448 creator A5061762260 @default.
• W207905448 date "2000-01-01" @default.
• W207905448 modified "2023-09-24" @default.
• W207905448 title "A new clustering algorithm for segmentation of magnetic resonance images" @default.
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