基于Gmac模型的乳腺肿块分割算法

The Segmentation Algorithm of Mammographic Masses Based on the Gmac Model

在乳腺CAD系统中,乳腺肿块分割是一个重要的先前步骤,分割结果的好坏直接影响到肿块的分类和检测.本文将Gmac模型应用到乳腺肿块分割上,并提出了求解Gmac模型的两种改进方法:改进的变分水平集法、改进的splitbregman方法.实验选取了483幅医学乳腺肿块图片进行分割,得到了两种改进方法的CM均值分别为64%和76%;AMED均值分别为4.4750和1.4602.结果表明:改进的split bregman方法对乳腺肿块进行了更有效的分割.实验也利用经典的ACWE模型和GAC模型对上述乳腺肿块图片进行了分割实验,与基于改进split bregman方法的Gmac模型相比,结果表明:Gmac模型具有更好的分割性能.

In breast-CAD system ,the segmentation of breast mass is an important step for pretreatment .In this work ,the Gmac model is applied for breast mass segmentation ,meanwhile ,two improved methods to solve the Gamc model are proposed ：the improved Level-set algorithm and the improved split-bregman algorithm .In experiment part ,we select 483 medical images for test . The CM for the two algorithms is 64% and 76% and AMED is 4.4750 and 1.4602 respectively .The experimental results show the improved split bregman method is more effective for breast-mass segmentation .Besides ,the ACWE model and GAC model are used for comparison and the results indicate that the Gmac model is more suitable for breast-mass segmentation .