摘要
目的提出一种对数字乳腺影像计算机辅助诊断中可疑密度分割更为有效的分割方法。方法使用基础的边缘分割算子sobel和离散形式的动态轮廓模型对乳腺影像中的可疑密度区域(肿块)进行两步法分割,边缘检测进行带阈值选择的轮廓初步提取,然后采用部分边缘点作为动态轮廓模型的计算点,获得能量收敛的最终轮廓。结果实现对数字乳腺影像库和乳腺体模影像的分割,并对分割轮廓进行与人工分割轮廓的重叠率计算和ROC曲线计算,对算法进行评价。结论最终分割结果有效降低假阳性概率,提高了分割的特异性。
Objective： To develop and evaluate a more effective segmentation algorithm in computer-aided detection on masses of mammograms. Methods： In this work, a two-stage mass segmentation method was used in pixel-level segmentation and region-level segmentation which was sensitive in detection the suspicious densities （mass） in digital mammograms. Results： The method was used to segment a consecutive set of 62 digital mammograms taken from the Visible Human Data （VHD） and rnammogram phantom＇ s image. Evaluation of the performance of the method was done in two different ways. In the first experiment, the segmentations of masses were compared with annotations made by the radiologists. In the second experiment, the ROC curves were calculated to examine the segmentation results. Conclusion： The performance of two-stage mass segmentation can reject false-positive regions, and thus the specificity increases while high sensitivity is maintained, and the suspicious areas can be segmented more closely.
出处
《泰山医学院学报》
CAS
2005年第2期136-139,共4页
Journal of Taishan Medical College
关键词
数字乳腺影像
计算机辅助诊断
分割
假阳性
digital mammography
computer-aided detection （CAD）
segmentation
false positive reduction
