小邻域统计信息核磁共振医学图像分割模型

张建伟; 方林; 陈允杰; 詹天明

doi:10.11834/jig.20140217

医学图像处理 | 浏览量 : 0 下载量: 593 CSCD: 0

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专辑

小邻域统计信息核磁共振医学图像分割模型
Magnetic resonance medical images segmentation based on a local statistical information model
2014年19卷第2期页码：305-312
网络出版：2014-01-28，

纸质出版：2014
DOI： 10.11834/jig.20140217
稿件说明：

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张建伟, 方林, 陈允杰, 詹天明. 小邻域统计信息核磁共振医学图像分割模型[J]. 中国图象图形学报, 2014,19(2):305-312. DOI： 10.11834/jig.20140217.

Zhang Jianwei, Fang Lin, Chen Yunjie, Zhan Tianming. Magnetic resonance medical images segmentation based on a local statistical information model[J]. Journal of Image and Graphics, 2014, 19(2): 305-312. DOI： 10.11834/jig.20140217.

摘要

提出局部统计信息测地线活动轮廓图像分割方法。该方法采用高斯分布拟合图像局部灰度统计特征信息，构造了方向性驱动项。在此基础上，建立了局部统计信息测地线能量泛函。通过极小化该泛函，来驱动演化曲线有序地向目标边界逼近，最后，整个分割过程采用二值水平集方法实现。本文方法降低了灰度不均匀信息影响，达到提取感兴趣区域轮廓目的，提高算法效率和稳定性。实验结果表明，该方法可以快速准确地分割医学感兴趣目标边界。

Abstract

In this paper

we propose a local statistical geodesic active contour (GAC) image segmentation method. Local intensity statistical information according with the Gaussian distribution is assumed. A directional driving item is established in order to reduce the effect of intensity inhomogeneity information. Second

a local statistical geodesic active contour energy function based on this hypothesis is established. By minimizing the proposed energy functional

it can orderly guide the movement of evolution curve to object boundaries for achieving regions of interest (ROI) segmentation. Finally

the method is implemented by a binary level set function in order to improve the algorithm's efficiency and stability. Experiment results with medical images show that the algorithm can segment ROI of medical images fast and accurate.