Active contours driven by Cuckoo Search strategy for brain tumour images segmentation
Active contours driven by Cuckoo Search strategy for brain tumour images segmentation
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DOI：
10.1016/j.eswa.2016.02.048
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发表时间：
2016-09-01
影响因子：
8.5
通讯作者：
Chalopin, Claire
中科院分区：
文献类型：
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作者：
Ilunga-Mbuyamba, Elisee;Mario Cruz-Duarte, Jorge;Chalopin, Claire
提出了一种基于多种群布谷鸟搜索算法的主动轮廓模型。这种策略有助于控制点收敛到能量函数的全局最小值，而不像传统的ACM版本经常陷入局部最小值。在所提出的方法中，每个控制点被约束在一个本地搜索窗口，其能量最小化是通过布谷鸟搜索通过Levy航班范例。对于局部搜索窗口，考虑了两种形状方法：矩形和极坐标。结果表明，使用极坐标的CS方法通常优于在矩形形状中进行的CS。采用真实的医学图像和人工合成图像，通过Jaccard指数、Dice指数和Hausdorff距离三个性能指标对所提出的策略进行了验证。特别适用于磁共振成像（MRI）图像，所提出的方法能够达到更好的精度性能比传统的ACM配方，也被称为蛇和使用多种群粒子群优化（PSO）算法。(C)2016爱思唯尔有限公司版权所有。
In this paper, an alternative Active Contour Model (ACM) driven by Multi-population Cuckoo Search (CS) algorithm is introduced. This strategy assists the converging of control points towards the global minimum of the energy function, unlike the traditional ACM version which is often trapped in a local minimum. In the proposed methodology, each control point is constrained in a local search window, and its energy minimisation is performed through a Cuckoo Search via Levy flights paradigm. With respect to local search window, two shape approaches have been considered: rectangular shape and polar coordinates. Results showed that the CS method using polar coordinates is generally preferable to CS performed in rectangular shapes. Real medical and synthetic images were used to validate the proposed strategy, through three performance metrics as the Jaccard index, the Dice index and the Hausdorff distance. Applied specifically to Magnetic Resonance Imaging (MRI) images, the proposed method enables to reach better accuracy performance than the traditional ACM formulation, also known as Snakes and the use of Multi-population Particle Swarm Optimisation (PSO) algorithm. (C) 2016 Elsevier Ltd. Ail rights reserved.