CAJ | 학술논문

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.
목적제출일충대수자유선영상계산궤보조진단중가의밀도분할경위유효적분할방법.방법사용기출적변연분할산자sobel화리산형식적동태륜곽모형대유선영상중적가의밀도구역(종괴)진행량보법분할,변연검측진행대역치선택적륜곽초보제취,연후채용부분변연점작위동태륜곽모형적계산점,획득능량수렴적최종륜곽.결과실현대수자유선영상고화유선체모영상적분할,병대분할륜곽진행여인공분할륜곽적중첩솔계산화ROC곡선계산,대산법진행평개.결론최종분할결과유효강저가양성개솔,제고료분할적특이성.
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 mammogram 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.