Standard methods to quantify image quality (IQ) may not be adequate for clinical images since they depend on uniform backgrounds and linearity. Statistical model observers are not restricted to these limitations and might be suitable for IQ evaluation of clinical images. One of these statistical model observers is the channelized Hotelling observer (CHO), where the images are filtered by a set of channels. The aim of this study was to evaluate six different channel sets, with an additional filter to simulate the human contrast sensitivity function (CSF), in their ability to predict human observer performance. For this evaluation a two alternative forced choice experiment was performed with two types of background structures (white noise (WN) and clustered lumpy background (CLB)), 5 disk-shaped objects with different diameters and 3 different signal energies. The results show that the correlation between human and model observers have a diameter dependency for some channel sets in combination with CLBs. The addition of the CSF reduces this diameter dependency and in some cases improves the correlation coefficient between human- and model observer. For the CLB the Partial Least Squares channel set shows the highest correlation with the human observer (r²=0.71) and for WN backgrounds it was the Gabor-channel set with CSF (r²=0.72). This study showed that for some channels there is a high correlation between human and model observer, which suggests that the CHO has potential as a tool for IQ analysis of digital mammography systems.

Date Published: 17 March 2015
PDF: 9 pages
Proc. SPIE 9416, Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment, 94160Z (17 March 2015); doi: 10.1117/12.2081528

Published in SPIE Proceedings Vol. 9416:
Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment
Claudia R. Mello-Thoms; Matthew A. Kupinski, Editor(s)