Segmentation is an essential requirement in medicine when digital images are used in illness diagnosis, especially, in posterior tasks as analysis and disease identification. An efficient segmentation of brain Magnetic Resonance Images (MRIs) is of prime concern to radiologists due to their poor illumination and other conditions related to de acquisition of the images. Thresholding is a popular method for segmentation that uses the histogram of an image to label different homogeneous groups of pixels into different classes. However, the computational cost increases exponentially according to the number of thresholds. In this paper, we perform the multi-level thresholding using an evolutionary metaheuristic. It is an improved version of the Harris Hawks Optimization (HHO) algorithm that combines the chaotic initialization and the concept of altruism. Further, for fitness assignment, we use a hybrid objective function where along with the cross-entropy minimization, we apply a new entropy function, and leverage weights to the two objective functions to form a new hybrid approach. The HHO was originally designed to solve numerical optimization problems. Earlier, the statistical results and comparisons have demonstrated that the HHO provides very promising results compared with well-established metaheuristic techniques. In this article, the altruism has been incorporated into the HHO algorithm to enhance its exploitation capabilities. We evaluate the proposed method over 10 benchmark images from the WBA database of the Harvard Medical School and 8 benchmark images from the Brainweb dataset using some standard evaluation metrics.
翻译:当数字图像用于疾病诊断,特别是用于分析和疾病识别等后期任务时,数字图像的分解是医学的一项基本要求。脑磁共振成像(MRIs)的有效分解是放射学家们最关心的一个问题,因为他们的光化差以及与脱脱脱获取图像有关的其他条件。 分解是一种常用的分解方法,它使用图像的直方图将不同的同质像群标记为不同类别。 但是,根据阈值的数量,计算成本会急剧上升。 在本文中,我们使用进化的计量经济学来进行多层次的临界值。这是哈里斯·霍克斯最佳化(HHHHHO)算法的改进版本,结合混乱的初始化和利他主义概念。此外,为了健身任务,我们使用混合目标函数,将不同的等同的像群划成不同的类。我们应用了新的昆虫功能,并将拟议重量用于两项目标函数,形成一个新的混合方法。HHHHO最初设计用于解决数字整齐问题。 早期,统计结果和对比HHO HHO的算法已经很好地将其纳入了10号的标准数据库。我们已经把HHOBAHR的实验室的图与AL方法比了。