【摘要】 为把数字乳腺机输出的原始图像分类为背景区域和乳腺区域2部分,以提高医生阅片质量与效率或输出到计算机辅助诊断(CAD)系统以便做进一步处理,提出了一种数字乳腺影像边缘轮廓识别的分类模板构造算法。该算法首先在低分辨率下采用最优阈值及形态学方法对数字乳腺进行初始分割,并使用面积分类器识别出最大面积目标(乳腺区域),然后在高分辨率下采用Dijkstra最小代价搜索算法准确获取乳腺区域封闭轮廓,最后构造分类模板。通过实验表明,该算法是一种快速、准确、稳健的数字影像边缘轮廓识别算法。更多还原

【Abstract】 The digital mammary machine output of an original image is classified as two parts,background region and breast area. so as to help radiologist screening,and it is also an essential preprocessing step for computer aided diagnosis(CAD). This paper proposed a fast classification mask construction algorithm. First,initial breast segmentation with optimal threshold combined with an area classifier at low image resolution was performed,then Dijkstra searching method was used to retrieve the accurate breast profile at the original resolution,finally,a classification mask for the mammogram was constructed. Experiment results show this algorithm can extract breast profile region from raw mammogram data fast,robustly and accurately.更多还原