基于小波能量嵌入水平集的医学图像高度相似区域分割方法

F. Alim-Ferhat, A. Boudjelal, S. Seddiki, B. Hachemi, S. Oudjemia
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引用次数: 2

摘要

本文提出了一种新的分割方法,该方法融合了一种新的特征,可以增强区域之间的不相似性。结合这一特征,形成了一种新的基于水平集的活动轮廓模型,该模型解决了高度相似强度区域之间没有明确边界的分割问题。利用小波变换的幂表示新特征,称为小波能量。在这个公式中,引导轮廓的两个项是小波能量融合区域项和轮廓平滑项。在此基础上,推导了轮廓的演化方程,并在MATLAB中进行了实现。这种分割方法被称为小波能量嵌入水平集法。实验结果表明,该方法能够分割出与背景强度相似度较高的感兴趣区域。
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Wavelet Energy Embedded into a Level Set Method for Medical Images Segmentation in the Presence of Highly Similar Regions
This paper is motivated by present a new segmentation method that integrates a novel feature, which is able to enhance the dissimilarity between regions. This feature is integrated to formulate a new level set based active contour model, which addresses the segmentation of regions with highly similar intensities, which do not have clear boundaries between them. The power of wavelet transform is adapted to formulate the new feature, named as wavelet energy. In this formulation, the two terms that guide the contour are the wavelet energy incorporated region term and the contour smoothness term. With this formulation, the equations for evolving the contour are derived and implemented in MATLAB. This segmentation method is named Wavelet energy Embedded into a level set method. The experimental results show that the proposed method is able to segment the region of interest that have high similarity in intensities with their background.
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