基于模型的乳腺X线图像胸肌分割算法研究

针对目前的乳腺X线图像胸肌分割算法自适应能力较弱的问题,提出了一种基于胸肌模型的新算法.该算法将一组不同尺寸的感兴趣区(ROI)作用到乳腺X线图像,在每个ROI中用迭代阈值法得到最优阈值组成一条曲线,并计算出与该最优阈值曲线对应的局部均方差曲线,根据提出的近似真实乳腺X线图像胸肌模型的特征,自动确定乳腺X线图像中胸肌区域的最佳分割阈值.最后使用两段直线粗拟合和多边形精拟合,提取阈值化后的胸肌边界.实验结果表明该算法可以显著地提高胸肌分割的精度,并且具有较好的鲁棒性.

Study on model-based pectoral-muscle segment algorithm in mammograms

To overcome the low adaptability of pectoral-muscle segmentation in mammograms, a novel model-based algorithm was presented. Regions of interest (ROI) with different sizes were applied on a mammogram respectively, and optimal thresholds were calculated with iterative thresholding algorithm in each of ROI. Composing an optimal threshold curve, thresholds generated a local mean-square deviation curve accordingly. Based on two pectoral muscle models, the optimal segment threshold of the pectoral muscle was extracted from the optimal threshold curve and local mean-square deviation curve. By using two-segment line fitting and polygon approaching technique, the edge of the pectoral muscle was obtained. Experimental results show that this algorithm has higher accuracy and adaptability than conventional methods.