作　　者： ; ; ; ;

机构地区： 浙江大学计算机科学与技术学院

出　　处： 《计算机研究与发展》 2009年第9期1438-1445,共8页

摘　　要： 乳腺钼靶片上的微钙化点簇是早期乳腺癌的重要信号,目前,无论是采用人工阅片或是计算机辅助诊断系统都很难对微钙化点簇进行可靠的检测.提出了一种基于二维粒子的自动检测乳腺钼靶片上微钙化点簇的方法,以二维粒子为单位进行可疑区域的提取和微钙化点的判别,很好地克服了传统的基于像素级别的检测方法容易受到干扰和基于数学形态学的检测方法很难确定合适结构元素的问题.提出的快速多元分割算法克服了基于经典Fast Marching的多元分割算法在乳腺钼靶片上进行二维粒子分割时运算时间过长的问题,显著提高了二维粒子的分割速度.在DDSM数据库上的实验结果表明,新的检测方法具有比较满意的检测精度和处理速度. Clustered microcalcifications （MCs） in mammograms can be an important early sign of breast cancer for women and are still very difficult to reliably detect by either radiologists or computeraided diagnosis （CAD） system. Traditional filtering based detection methods are vulnerable to complex textures on mammograms. And morphology based methods have some problems in determining the structure elements. To overcome these shortcomings, a novel detection method based on 2-dimensional particles is proposed in this paper. First, a new segmentation algorithm is used to obtain 2-dimensional particles on mammograms. Then suspicious particles are picked up according to their contrasts and background intensities. An inpainting based method is applied to extract features from these suspicious particles, followed by using a classifier to separate the microcalcifications from normal particles. Experiments on the DDSM database show that the new detection method can obtain 93.3% true positive rate with average 3.1 false positives per image. Compared with the original fast marching algorithm, the new segmentation method proposed greatly reduces the time consumption in 2-dimensional particles segmentation on mammograms. Experiments on.the DDSM database show that the average time consumption of the new segmentation method decrease by nearly nineteen-twentieth. The new segmentation method presented can also be used in other image processing applications.

关 键 词： 二维粒子 图像分割 微钙化点 乳腺钼靶片

领　　域： [自动化与计算机技术] [自动化与计算机技术]