Feature saliency analysis for perceptual similarity of clustered microcalcifications
Retrieving and presenting a set of known lesions similar to the one being evaluated has the potential to improve the performance of radiologists in diagnosis of breast cancer with clustered microcalcifications (MCs). In this work, we investigate how perceptually similar cases are related to each other in terms of their image features. We apply supervised learning and feature saliency analysis to determine the most relevant image features based on similarity ratings collected from a group of radiologists on 1,000 image pairs. The results demonstrate that the relevant features are consistent in radiologists' similarity ratings among different lesions, which include geometric clustering features (size and shape) and MC features (size, contrast and shape).