局部熵驱动的GAC模型在生物医学图像分割中的应用  

Application of GAC Model Driven by the Local Entropy on Medical Image Segmentation

作　　者：王顺凤[1] 冀晓娜[1] 张建伟[1] 陈允杰[1] 方林[1] 

机构地区：[1]南京信息工程大学数学与统计学院,江苏南京210044

出　　处：《电子学报》2013年第12期2487-2492,共6页Acta Electronica Sinica

基　　金：国家自然科学基金项目(No.61173072);国家自然科学青年基金项目(No.61003209);江苏省自然科学基金项目(No.BK2011824);江苏省高校自然科学研究项目(No.10KJB520012)

摘　　要：针对测地线活动轮廓(GAC)模型不能对包含噪声和灰度不均匀现象这类复杂背景图像成功提取目标的问题,本文提出局部熵驱动的GAC模型.首先提取图像的局部信息熵来刻画图像的灰度变化,再利用局部熵构造符号压力函数来指导轮廓曲线向目标边界靠近,实现目标的分割.为降低计算复杂度并提高模型对水平集变化的鲁棒性,采用二值水平集方法进行求解.实验结果表明,本文方法可以克服噪声和灰度不均匀对图像分割的影响,实现快速准确的分割.The geodesic active contour model （GAC）can′t identify the object of the images with complex background such as noise and intensity inhomogeneities successfully .For this reason ,this paper proposes GAC model driven by the local entropy .First of all ,the local information entropy of image is abstracted to describe the local intensity variation .Then ,the signed pressure force func-tion based on the local entropy are structured ,which guides the contour curve close to the boundary of the object and achieves the segmentation of the object .In order to reduce the computational complexity and improve the robustness of the proposed model to different level sets ,the proposed method is implemented by the binary level set method .The experimental results show that this method can overcome the influence of complex background to the segmentation results ,and realize fast and accurate segmentation .

关 键 词：测地线活动轮廓模型 局部熵 符号压力函数 二值水平集方法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]