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A multi-objective optimization approach for brain MRI segmentation using fuzzy entropy clustering and region-based active contour methods

In this paper, we present a new multi-objective optimization approach for segmentation of Magnetic Resonance Imaging (MRI) of the human brain. The proposed algorithm not only takes advantages but also solves major drawbacks of two well-known complementary techniques, called fuzzy entropy clustering method and regionbased active contour method, using multi-objective particle swarm optimization (MOPSO) approach. In order to obtain accurate segmentation results, firstly, two fitness functions with independent characteristics, compactness and separation, are derived from kernelized fuzzy entropy clustering with local spatial information and bias correction (KFECSB) and a novel adaptive energy weight combined with global and local fitting energy active contour (AWGLAC) model. Then, they are simultaneously optimized to finally produce a set of non-dominated solutions, from which L2-metric method is used to select the best trade-off solution. Our algorithm is both verified and compared with other state-of-the-art methods using simulated MR images and real MR images from the McConnell Brain Imaging Center (BrainWeb) and the Internet Brain Segmentation Repository (IBSR), respectively. The experimental results demonstrate that the proposed technique achieves superior segmentation performance in terms of accuracy and robustness. 1. Introduction Image segmentation is the process of partitioning an image space into non-overlapped meaningful homogeneous regions or objects, according to given quantitative criteria: gray level, color, texture or combination of them [1]. For medical image analysis, the success of an image analysis system depends heavily on the quality of segmentation. We can find it in many real-life applications, for instance, in neurodegenerative disorders such as Alzheimer's disease, in movement disorders such as Parkinson's or Parkinson related syndrome, in congential brain malformations or perinatal brain damage, or in post-traumatic syndrome. However, the input MR brain images, which contain complex structures, are inherently noisy and often corrupted by intensity non-uniformity

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