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Classification of Visual Signs in Abdominal CT Image Figures in Biomedical Literature.
“Imaging signs” are a critical part of radiology’s language. They not only are important for conveying diagnosis, but may also aid in indexing radiology literature and retrieving relevant cases and images. Here we report our work towards representing and categorizing imaging signs of abdominal abnormalities in figures in the radiology literature. Given a region-of-interest (ROI) from a figure, our goal was to assign a correct imaging sign label to that ROI from the following seven: accordion, comb, ring, sandwich, small bowel feces, target, or whirl. As training and test data, we created our own “gold standard” dataset of regions containing imaging signs. We computed 2997 feature attributes to represent imaging sign characteristics for each ROI in training and test sets. Following feature selection they were reduced to 70 attributes and were input to a Support Vector Machine classifier. We applied image-enhancement methods to compensate for variable quality of the images in radiology articles. In particular we developed a method for automatic detection and removal of pointers/markers (arrows, arrowheads, and asterisk symbols) on the images. These pointers/markers are valuable for approximately locating ROIs; however, they degrade the classification because they are often (partially) included in the training ROIs. On a test set of 283 ROIs, our method achieved an overall accuracy of 70% in labeling the seven signs, which we believe is a promising result for using imaging signs to search/retrieve radiology literature. This work is also potentially valuable for the creation of a visual ontology of biomedical imaging entities.