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Proceedings Paper

Observer-driven texture analysis in CT imaging
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Paper 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.

Paper Details

Date Published: 16 March 2020
PDF: 5 pages
Proc. SPIE 11316, Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, 1131610 (16 March 2020); doi: 10.1117/12.2549042
Show Author Affiliations
Matthew A. Kupinski, Univ. of Arizona (United States)
Zachary Garrett, Univ. of Arizona (United States)
Jiahua Fan, GE Healthcare (United States)


Published in SPIE Proceedings Vol. 11316:
Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment
Frank W. Samuelson; Sian Taylor-Phillips, Editor(s)

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