基于人工蜂群优化的MR图像分割算法研究

doi:10.7517/issn.1674-0475.191119

影像科学与光化学 ›› 2020, Vol. 38 ›› Issue (3): 508-513.DOI: 10.7517/issn.1674-0475.191119

基于人工蜂群优化的MR图像分割算法研究

曲蕴慧¹, 陈小菊²

1. 西安医学院卫生管理学院计算机教研室, 陕西西安 710021;
2. 西安医学院卫生管理学院, 陕西西安 710021

收稿日期:2019-11-20 出版日期:2020-05-15 发布日期:2020-05-15
通讯作者: 曲蕴慧
基金资助:
陕西省卫计委2018卫生健康科研项目（2018D078）和西安医学院配套基金项目（2018PT54）资助

Research on MR Image Segmentation Algorithm Based on Artificial Bee Colony Optimization

QU Yunhui¹, CHEN Xiaoju²

1. Computer Teaching and Research Section, School of Health Services Management, Xi'an Medical University, Xi'an 710021, Shaanxi, P. R. China;
2. School of Health Services Management, Xi'an Medical University, Xi'an 710021, Shaanxi, P. R. China

Received:2019-11-20 Online:2020-05-15 Published:2020-05-15

摘要/Abstract

摘要： 针对传统图像阈值分割算法在MR图像分割时存在的易受采集图像灰度不均、医学图像易受噪声干扰，因而难以得到准确分割阈值的问题，本文将人工蜂群算法与二维OSTU阈值分割算法相结合，提出一种基于人工蜂群优化的MR图像分割算法。使用医学图像的离散度矩阵的迹作为人工蜂群优化的目标函数，得到二维OSTU的最佳分割阈值；根据得到的最佳阈值，对图像采用二维OSTU分割的方法进行分割。实验结果证明，对于医学MR图像，本文所提出的算法具有精度高和鲁棒性强的特点，能够得到精确的分割后图像。

关键词: 人工蜂群算法, 二维OSTU, MR图像

Abstract: Traditional image threshold segmentation algorithm in MR image segmentation is easy to be disturbed by the problem of gray un-uniform and being susceptible to noise interference. Considering these problems, an MR image segmentation algorithm based on artificial bee colony optimization is proposed, which is combining the artificial bee colony algorithm with the two-dimensional OSTU threshold segmentation algorithm. The algorithm uses the trace of the dispersion matrix of medical image as the objective function of artificial bee colony optimization, the optimal segmentation threshold of two-dimensional OSTU is obtained. According to the optimal threshold, the image is segmented by two-dimensional OSTU. The experimental results show that, for medical MR images, the algorithm proposed in this paper has the characteristics of high accuracy and strong robustness, and can get accurate segmented images.

Key words: artificial bee colony, 2D OSTU, MR image

曲蕴慧, 陈小菊. 基于人工蜂群优化的MR图像分割算法研究[J]. 影像科学与光化学, 2020, 38(3): 508-513.

QU Yunhui, CHEN Xiaoju. Research on MR Image Segmentation Algorithm Based on Artificial Bee Colony Optimization[J]. Imaging Science and Photochemistry, 2020, 38(3): 508-513.

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基于人工蜂群优化的MR图像分割算法研究

Research on MR Image Segmentation Algorithm Based on Artificial Bee Colony Optimization

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