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基于二进小波变换和活动轮廓模型的医学图像分割

[Segmentation of medical images based on dyadic wavelet transform and active contour model].

作者信息

Li Hong, Wang Huinan, Chang Linfeng, Shao Xiaoli

机构信息

College of Automation Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China.

出版信息

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2008 Dec;25(6):1276-81.

Abstract

The interference of noise and the weak edge characteristic of symptom information on medical images prevent the traditional methods of segmentation from having good effects. In this paper is proposed a boundary detection method of focus which is based on dyadic wavelet transform and active contour model. In this method, the true edge points are detected by dyadic wavelet transform and linked by improved fast active contour model algorithm. The result of experiment on MRI of brain shows that the method can remove the influence of noise effective and detect the contour of brain tumor actually.

摘要

医学图像中噪声的干扰以及症状信息的弱边缘特征使得传统的分割方法效果不佳。本文提出了一种基于二进小波变换和主动轮廓模型的病灶边界检测方法。该方法通过二进小波变换检测真实边缘点,并利用改进的快速主动轮廓模型算法将其连接起来。脑部磁共振成像(MRI)实验结果表明,该方法能有效消除噪声影响,准确检测出脑肿瘤轮廓。

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