Clarke L P, Kallergi M, Qian W, Li H D, Clark R A, Silbiger M L
Department of Radiology, College of Medicine, University of South Florida, Tampa 33612-4799.
Cancer Lett. 1994 Mar 15;77(2-3):173-81. doi: 10.1016/0304-3835(94)90100-7.
A novel algorithm was developed for computer aided diagnosis of microcalcification clusters in digital mammography. The method includes: (a) tree-structured central weighted median filters with variable shape windowing to suppress image noise but preserve image details; (b) a quasi range dispersion edge detector to increase edge contrast and definition; and (c) tree-structured wavelets for calcification segmentation. The preliminary evaluation of the method on nine mammograms showed that 100% sensitivity can be achieved at the expense of four false positive clusters per image. Research is ongoing for further optimization of the algorithm to reduce the number of false alarms and establish its clinical value.
开发了一种用于数字乳腺摄影中微钙化簇计算机辅助诊断的新算法。该方法包括：(a) 具有可变形状窗口的树形结构中心加权中值滤波器，用于抑制图像噪声但保留图像细节；(b) 准范围色散边缘检测器，用于提高边缘对比度和清晰度；以及 (c) 用于钙化分割的树形结构小波。该方法在九幅乳腺造影片上的初步评估表明，以每幅图像四个假阳性簇为代价可实现 100% 的灵敏度。正在进行研究以进一步优化该算法，以减少误报数量并确定其临床价值。
