Iby and Aladar Fleischman Faculty of Engineering, Tel-Aviv University, Tel-Aviv 69978, Israel.
IEEE Trans Biomed Eng. 2013 Aug;60(8):2253-61. doi: 10.1109/TBME.2013.2252464. Epub 2013 Mar 13.
The screening mammography is currently the best procedure available for early detection of the breast cancer. The acquired mammograms are high dynamic range (HDR) images having a 12 bit grayscale resolution. When viewed by a radiologist, a single image must be examined several times, each time focusing on a different intensity range. We have developed a biologically derived mammography companding (BDMC) algorithm for compression, expansion, and enhancement of mammograms, in a fully automatic way. The BDMC is comprised of two main processing stages: 1) preliminary processing operations which include standardization of the intensity range and expansion of the intensities which belong to the low intensity range. 2) Adaptively companding the HDR range by integrating multiscale contrast measures. The algorithm's performance has been preliminarily clinically tested on dozens of mammograms in collaboration with experienced radiologists. It appears that the suggested method succeeds in presenting all of the clinical information, including all the abnormalities, in a single low dynamic range companded image. This companded and enhanced image is not degraded more than the HDR image and can be analyzed without the need for professional workstation and its specific enhancement software.
乳腺 X 光筛查目前是早期发现乳腺癌的最佳手段。所获取的乳腺 X 光片是具有 12 位灰度分辨率的高动态范围 (HDR) 图像。当由放射科医生查看时，必须多次检查单个图像，每次都要关注不同的强度范围。我们已经开发了一种基于生物学的乳腺 X 光压缩扩展 (BDMC) 算法，用于以全自动方式对乳腺 X 光片进行压缩、扩展和增强。BDMC 由两个主要处理阶段组成：1）初步处理操作，包括强度范围的标准化和属于低强度范围的强度的扩展。2）通过整合多尺度对比度度量来自适应地压缩 HDR 范围。该算法已与经验丰富的放射科医生合作，在几十张乳腺 X 光片上进行了初步临床测试。结果表明，该方法成功地在单个低动态范围扩展图像中呈现了所有的临床信息，包括所有异常。该扩展和增强的图像不会比 HDR 图像降级更多，并且可以在不需要专业工作站及其特定增强软件的情况下进行分析。
