@inproceedings{70913b91d1b745a7bbd6c46754453eec,
title = "Observer-driven texture analysis in CT imaging",
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.",
keywords = "CT imaging, Image quality, Model observers, Texture analysis",
author = "Kupinski, {Matthew A.} and Zachary Garrett and Jiahua Fan",
note = "Publisher Copyright: {\textcopyright} 2020 SPIE.; Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment ; Conference date: 19-02-2020 Through 20-02-2020",
year = "2020",
doi = "10.1117/12.2549042",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Samuelson, {Frank W.} and Sian Taylor-Phillips",
booktitle = "Medical Imaging 2020",
}