Regentova Emma, Zhang Lei, Zheng Jun, Veni Gopalkrishna
Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, 4505 Maryland Parkway, Las Vegas, Nevada 89154, USA.
Med Phys. 2007 Jun;34(6):2206-19. doi: 10.1118/1.2733800.
In this paper we investigate the performance of statistical modeling of digital mammograms by means of wavelet domain hidden Markov trees for its inclusion to a computer-aided diagnostic prompting system. The system is designed for detecting clusters of microcalcifications. Their further discrimination as benign or malignant is to be done by radiologists. The model is used for segmenting images based on the maximum likelihood classifier enhanced by the weighting technique. Further classification incorporates spatial filtering for a single microcalcification (MC) and microcalcification cluster (MCC) detection. Contrast filtering applied for the digital database for screening mammography (DDSM) dataset prior to spatial filtering greatly improves the classification accuracy. For all MC clusters of 40 mammograms from the mini-MIAS database of Mammographic Image Analysis Society, 92.5%-100% of true positive cases can be detected under 2-3 false positives per image. For 150 cases of DDSM cases, the designed system is capable to detect up to 98% of true positives under 3.3% of false positive cases.
在本文中，我们通过小波域隐马尔可夫树研究数字乳腺X线照片的统计建模性能，以便将其纳入计算机辅助诊断提示系统。该系统旨在检测微钙化簇。它们是良性还是恶性的进一步鉴别将由放射科医生进行。该模型用于基于通过加权技术增强的最大似然分类器对图像进行分割。进一步的分类包括用于单个微钙化（MC）和微钙化簇（MCC）检测的空间滤波。在空间滤波之前对用于筛查乳腺X线摄影的数字数据库（DDSM）数据集应用对比度滤波可大大提高分类准确性。对于来自乳腺影像分析协会小型MIAS数据库的40幅乳腺X线照片的所有MC簇，在每幅图像2 - 3个假阳性的情况下，可以检测到92.5% - 100%的真阳性病例。对于150例DDSM病例，所设计的系统能够在3.3%的假阳性病例下检测到高达98%的真阳性病例。
