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Liver segmentation in MRI images based on whale optimization algorithm

机译:基于鲸鱼优化算法的MRI图像肝脏分割

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This paper proposes an approach for liver segmentation in MRI images based on Whale optimization algorithm (WOA). It is used to extract the different clusters in the abdominal image to support the segmentation process. A statistical image is prepared to define the potential liver position in the abdominal image. Then, WOA divides the image into a predefined number of clusters. The prepared statistical image is converted into a binary image and multiplied by the image clustered by WOA. This multiplication process removes a great part of other organs from the image. It is followed by some points, picked up by user interaction, representing the required clusters which reside in the area of liver. The morphological operations enhance the initial segmented liver and produces the final image. The proposed approach is tested using a set of 70 MRI images, annotated and approved by radiology specialists. The resulting image is validated using structural similarity index measure (SSIM), similarity index (SI) and other five measures. The overall accuracy of the experimental result showed accuracy of 96.75% using SSIM and 97.5 using SI%.
机译:提出了一种基于鲸鱼优化算法(WOA)的MRI图像肝脏分割方法。它用于提取腹部图像中的不同簇,以支持分割过程。准备一个统计图像以定义腹部图像中潜在的肝脏位置。然后,WOA将图像划分为预定数量的群集。将准备好的统计图像转换为二进制图像,然后乘以WOA聚类的图像。这个乘法过程从图像中去除了很大一部分其他器官。它后面是一些点,这些点由用户交互拾取,代表驻留在肝脏区域中的所需簇。形态学操作增强了最初分割的肝脏并产生了最终图像。所提出的方法是使用一组70张MRI图像进行测试的,并由放射学专家进行了注释和批准。使用结构相似性指标度量(SSIM),相似性指标(SI)和其他五个度量来验证生成的图像。实验结果的整体准确性显示,使用SSIM的准确性为96.75%,使用SI%的准确性为97.5。

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