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16 March 2020 Observer-driven texture analysis in CT imaging
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Abstract
We have implemented a technique for analyzing and characterizing the textures in medical images. This technique generates a list of characteristic textures and sorts them from most important to least important for the task of detecting a specific signal in the image. The effects of the human-visual system can be incorporated into this method through the use of an eye filter. The final set of sorted textures can be quickly utilized to analyze new sets of images and make comparison regarding performance on the same task. This analysis is based upon whether the new set of images contains textures that are similar or dissimilar to that of the original set of images. We present the method for analyzing and sorting textures based on how well signals can be distinguished. We also discuss the importance of the most "obscuring" textures that make signal-detection difficult. Results and comparisons of task performance are presented.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Matthew A. Kupinski, Zachary Garrett, and Jiahua Fan "Observer-driven texture analysis in CT imaging", Proc. SPIE 11316, Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, 1131610 (16 March 2020); https://doi.org/10.1117/12.2549042
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