What catches a radiologist's eye? A comprehensive comparison of feature types for saliency prediction

Experienced radiologists are in short supply, and are sometimes called upon to read many images in a short amount of time. This leaves them with a limited amount of time to read images, and can lead to fatigue and stress which can be sources of error, as they overlook subtle abnormalities that they otherwise might not miss. Another factor in error rates is called satisfaction of search, where a radiologist misses a second (typically subtle) abnormality after finding the first. These types of errors are due primarily to a lack of attention to an important region of the image during the search. In this paper we discuss the use of eye tracker technology, in combination with image analysis and machine learning techniques, to learn what types of features catch the eye experienced radiologists when reading chest X-rays for diagnostic purposes, and to then use that information to produce saliency maps that predict what regions of each image might be most interesting to radiologists. We found that, out of 13 popular features types that are widely extracted to characterize images, 4 are particularly useful for this task: (1) Localized Edge Orientation Histograms (2) Haar Wavelets, (3) Gabor Filters, and (4) Steerable Filters.