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MR Brain Image Segmentation using Bacteria Foraging Optimization Algorithm

机译:基于细菌觅食优化算法的MR脑图像分割

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The most important task in digital image processing is image segmentation. This paper put forward an unique image segmentation algorithm that make use of a Markov Random Field (MRF) hybrid with biologically inspired technique Bacteria Foraging Optimization Algorithm (BFOA) for Brain Magnetic Resonance Images The proposed new algorithm works on the image pixel data and a regioneighborhood map to form a context in which they can merge. Hence, the MR brain image is segmented using MRF-BFOA and the results are compared to traditional metaheuristic segmentation method Genetic Algorithm. All the experiment results show that MRF-BFOA has better performance than that of standard MRF-GA
机译:数字图像处理中最重要的任务是图像分割。本文提出了一种独特的图像分割算法,该算法利用马尔可夫随机场(MRF)与生物启发技术的混合,对脑磁共振图像进行细菌觅食优化算法(BFOA)。提出的新算法适用于图像像素数据和区域/邻居地图以形成可以合并的上下文。因此,使用MRF-BFOA对MR脑图像进行分割,并将结果与​​传统的元启发式分割方法“遗传算法”进行比较。所有实验结果表明,MRF-BFOA的性能优于标准MRF-GA

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