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1 October 2011 Brain tumor segmentation in 3D MRIs using an improved Markov random field model
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Proceedings Volume 8285, International Conference on Graphic and Image Processing (ICGIP 2011); 828532 (2011) https://doi.org/10.1117/12.913743
Event: 2011 International Conference on Graphic and Image Processing, 2011, Cairo, Egypt
Abstract
Markov Random Field (MRF) models have been recently suggested for MRI brain segmentation by a large number of researchers. By employing Markovianity, which represents the local property, MRF models are able to solve a global optimization problem locally. But they still have a heavy computation burden, especially when they use stochastic relaxation schemes such as Simulated Annealing (SA). In this paper, a new 3D-MRF model is put forward to raise the speed of the convergence. Although, search procedure of SA is fairly localized and prevents from exploring the same diversity of solutions, it suffers from several limitations. In comparison, Genetic Algorithm (GA) has a good capability of global researching but it is weak in hill climbing. Our proposed algorithm combines SA and an improved GA (IGA) to optimize the solution which speeds up the computation time. What is more, this proposed algorithm outperforms the traditional 2D-MRF in quality of the solution.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sahar Yousefi, Reza Azmi, and Morteza Zahedi "Brain tumor segmentation in 3D MRIs using an improved Markov random field model", Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 828532 (1 October 2011); https://doi.org/10.1117/12.913743
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