论文标题
隐藏的马尔可夫随机字段和杜鹃搜索方法用于医疗图像分割
hidden markov random fields and cuckoo search method for medical image segmentation
论文作者
论文摘要
医学图像的分割是诊断过程中的重要组成部分。医师需要自动，健壮和有效的结果。隐藏的马尔可夫随机字段（HMRF）提供了强大的模型。后者将分割问题建模为能量函数的最小化。杜鹃搜索（CS）算法是最近受自然风格的元式算法之一。它显示了其在许多工程优化问题中的效率。在本文中，我们使用三种杜鹃搜索算法来实现医疗图像分割。
Segmentation of medical images is an essential part in the process of diagnostics. Physicians require an automatic, robust and valid results. Hidden Markov Random Fields (HMRF) provide powerful model. This latter models the segmentation problem as the minimization of an energy function. Cuckoo search (CS) algorithm is one of the recent nature-inspired meta-heuristic algorithms. It has shown its efficiency in many engineering optimization problems. In this paper, we use three cuckoo search algorithm to achieve medical image segmentation.
