Muramatsu Chisako, Li Qiang, Suzuki Kenji, Schmidt Robert A, Shiraishi Junji, Newstead Gillian M, Doi Kunio
Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, The University of Chicago, 5841 S. Maryland Avenue, MC 2026, Chicago, Illinois 60637.
Med Phys. 2005 Jul;32(7Part1):2295-2304. doi: 10.1118/1.1944913.
We investigated a psychophysical similarity measure for selection of images similar to those of unknown masses on mammograms, which may assist radiologists in the distinction between benign and malignant masses. Sixty pairs of masses were selected from 1445 mass images prepared for this study, which were obtained from the Digital Database for Screening Mammography by the University of South Florida. Five radiologists provided subjective similarity ratings for these 60 pairs of masses based on the overall impression for diagnosis. Radiologists' subjective ratings were marked on a continuous rating scale and quantified between 0 and 1, which correspond to pairs not similar at all and pairs almost identical, respectively. By use of the subjective ratings as "gold standard," similarity measures based on the Euclidean distance between pairs in feature space and the psychophysical measure were determined. For determination of the psychophysical similarity measure, an artificial neural network (ANN) was employed to learn the relationship between radiologists' average subjective similarity ratings and computer-extracted image features. To evaluate the usefulness of the similarity measures, the agreement with the radiologists' subjective similarity ratings was assessed in terms of correlation coefficients between the average subjective ratings and the similarity measures. A commonly used similarity measure based on the Euclidean distance was moderately correlated (r=0.644) with the radiologists' average subjective ratings, whereas the psychophysical measure by use of the ANN was highly correlated (r=0.798). The preliminary result indicates that a psychophysical similarity measure would be useful in the selection of images similar to those of unknown masses on mammograms.
我们研究了一种心理物理学相似性度量方法，用于选择与乳腺钼靶片上未知肿块图像相似的图像，这可能有助于放射科医生区分良性和恶性肿块。从为本研究准备的1445幅肿块图像中选取了60对肿块，这些图像由南佛罗里达大学从乳腺钼靶筛查数字数据库中获取。五位放射科医生根据诊断的总体印象对这60对肿块给出主观相似性评分。放射科医生的主观评分在连续评分量表上标记，并在0到1之间量化，分别对应完全不相似的对和几乎相同的对。以主观评分为“金标准”，确定了基于特征空间中对之间的欧几里得距离的相似性度量和心理物理学度量。为了确定心理物理学相似性度量，采用人工神经网络（ANN）来学习放射科医生的平均主观相似性评分与计算机提取的图像特征之间的关系。为了评估相似性度量的有用性，根据平均主观评分与相似性度量之间的相关系数评估与放射科医生主观相似性评分的一致性。一种常用的基于欧几里得距离的相似性度量与放射科医生的平均主观评分中度相关（r = 0.644），而使用ANN的心理物理学度量高度相关（r = 0.798）。初步结果表明，心理物理学相似性度量在选择与乳腺钼靶片上未知肿块图像相似的图像方面将是有用的。
