摘要
超声图像检测是当前乳腺癌诊断的主要辅助手段之一.为实现超声乳腺肿瘤的计算机自动辅助诊断,提出一种基于支持向量机(SVM)目标检测与水平集图像分割相结合的全自动肿瘤提取算法.首先提取超声图像训练集的分块特征来训练SVM分类器,对测试集图像进行检测得到可疑病灶区域;然后提取可疑区域边缘作为水平集的初始轮廓,使用加入Bhattacharyya距离项的Chan-Vese主动轮廓改进模型进行可疑病灶区域的轮廓演化,得到准确的轮廓;最后综合面积、位置、灰度、纹理等因素设计区域评价筛选准则,去除可疑病灶中的干扰区域,得到最终的肿瘤分割结果.在真实病例数据集上的测试结果表明,利用该算法在良恶性肿瘤检测分割中均有较好表现.
Ultrasonic technology is one of the most important diagnostic tools for breast cancer detection.In this paper,we propose a fully automatic detection and segmentation algorithm of masses on breast ultrasound images by using support vector machine and level sets,which can be summarized in four distinct steps： 1）a SVM classifier for tumor detection is trained based on both gray and textural features;2）all suspicious tumor regions,possibly including false positive regions,are detected with the trained SVM classifier;3）an improved Chan-Vese（CV）active contour model,which adds a Bhattacharyya distance item to CV level set energy functional and performs better on ultrasound images,is used to segment regions accurately;4）all regions are ranked by a score formula which combines area,position,gray level and textural information,and the highest score region is identified as the tumor region.The whole algorithm is completely automatic with no manual intervention.Experimental results demonstrate the high efficiency of the proposed method.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2012年第5期662-668,676,共8页
Journal of Computer-Aided Design & Computer Graphics
基金
国家"八六三"高技术研究发展计划(2008AA121805-1)
