文章基本信息

标题：Meta-heuristic Approaches for Active Contour Model based Medical Image Segmentation
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作者：Anita Sahoo ; Satish Chandra
期刊名称：International Journal of Advances in Soft Computing and Its Applications
印刷版ISSN：2074-8523
出版年度：2014
卷号：6
期号：2
出版社：International Center for Scientific Research and Studies
摘要：In last few decades, the application of biological methods and systems to the study and design of engineering systems and modern technologies have fascinated many researchers. Numerous mathematical and meta-heuristic algorithms for solving optimization problems have been developed and widely used in both theoretical study and practical applications. To realize the application of such meta-heuristics for problem solving, the problem exercised here is the medical image segmentation. Image segmentation has an imperative role in medical image analysis for computer aided diagnosis and classification. In this paper, a number of population based meta-heuristic algorithms are reviewed and analyzed. The paper focuses on the principles behind each algorithm, the issues in designing a hybrid framework in view of the application in medical image segmentation. It extends a platform to develop novel meta- heuristics, and utilize the hybrid framework for improved image segmentation
关键词：Medical Image Segmentation; Active Contour Model; Genetic ; Algorithm; Differential Evolution; Honey Bee Mating Optimization; Particle ; Swarm Optimization; Ant Colony Optimization; Firefly Algorithm