An Approach of Automatically Selecting Seed Point Based on Region Growing for Liver Segmentation

Yongquan Xia, Xiwang Xie, Xinwen Wu, Jun Zhi, Sihai Qiao
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引用次数: 1

Abstract

Liver region extraction in abdominal CT images is a very important research field, a method of liver segmentation based on region growing for automatic selection of seed points is proposed in this paper. Firstly, the original image is binarized, and the initial area of the liver is extracted by the maximum area measurement method; After that, the improved region growth algorithm was used to segment the liver, and the location of seed points was automatically obtained by finding the center of the maximum inscribed circle locked in the initial liver area, which was used as the basis for the selection of seed points; Finally, the segmented liver region is treated by morphological methods. The experimental results show that the approach effectively solves the problem of manually selecting seed points for regional growth, and can improve the efficiency and accuracy of seed point selection, which avoids the selection of seed points at the wrong positions such as edges or noise due to subjective factors.
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基于区域生长的肝分割种子点自动选择方法
肝脏区域提取是腹部CT图像中一个非常重要的研究领域,本文提出了一种基于区域生长的肝脏分割方法,用于种子点的自动选择。首先对原始图像进行二值化处理,采用最大面积测量法提取肝脏的初始面积;然后,利用改进的区域生长算法对肝脏进行分割,通过寻找锁定在初始肝脏区域内的最大内切圆的圆心,自动获得种子点的位置,作为种子点选择的依据;最后,对分割后的肝脏区域进行形态学处理。实验结果表明,该方法有效地解决了人工选择区域生长种子点的问题,提高了种子点选择的效率和准确性,避免了由于主观因素导致种子点选择在边缘或噪声等错误位置。
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