The structural similarity index (SSIM) family is a set of metrics that has demonstrated good agreement with human observers in tasks using reference images. These metrics analyze the viewing distance, edge information between the reference and the test images, changed and preserved edges, textures, and structural similarity of the images. Eight metrics based on that family are proposed. This new set of metrics, together with another eight well-known SSIM family metrics, was tested to predict human performance in some specific tasks closely related to the evaluation of radiological medical images. We used a database of radiological images, comprising different acquisition techniques (MRI and plain films). This database was distorted with different types of distortions (Gaussian blur, noise, etc.) and different levels of degradation. These images were analyzed by a board of radiologists with a double-stimulus methodology, and their results were compared with those obtained from the 16 metrics analyzed and proposed in this research. Our experimental results showed that the readings of human observers were sensitive to the changes and preservation of the edge information between the reference and test images, changes and preservation in the texture, structural component of the images, and simulation of multiple viewing distances. These results showed that several metrics that apply this multifactorial approach (4-G-SSIM, 4-MS-G-SSIM, 4-G-r*, and 4-MS-G-r*) can be used as good surrogates of a radiologist to analyze the medical quality of an image in an environment with a reference image.
A software tool is presented to merge CDMAM phantom images with real mammographic backgrounds. It allows SKE
tasks in uniform and in real backgrounds. This kind of tasks can be used to compare human, human visual metric or
model observer performance in detail detection using uniform or mammographic backgrounds.
As it is very well known, local characteristics of the structures in real mammographic backgrounds reduce the human
performance in contrast-detail detection tasks. In consequence that performance cannot be inferred from the data
acquired in white noise (flat) backgrounds such as a CDMAM phantom produces.
It is of interest to compare the response of a mammography system to the same set of signals, either embedded in flat or
in real backgrounds. This comparison achieves two goals. The first one is to analyze the variation of the recognition
threshold of the system for both backgrounds. The second one is to analyze the performance of a human observer or a
model observer over the same set of signals, varying the nature of the backgrounds.
The software tool presented here uses CDMAM images to merge with a region of interest selected from a real
mammography. This region as well as the mixing image method (basically adding or multiplying pixels) can be freely
selected by the user. In this work a set of measurements of 8 images has been analyzed. We can preview the variation of
the contrast-detail detection for a human observer and a human visual system metric (R*).
A software tool is presented to measure the geometric distortion in images obtained with X-ray systems that provides a
more objective method than the usual measurements over the image of a phantom with usual rulers. In a first step, this
software has been applied to mammography images and makes use of the grid included into the CDMAM phantom
(University Hospital Nijmegen).
For digital images, this software tool automatically locates the grid crossing points and obtains a set of corners (up to
237) that are used by the program to determine 6 different squares, at top, bottom, left, right and central positions. The
sixth square is the largest that can be fitted in the grid (widest possible square). The distortion is calculated as ((length of
left diagonal - length of right diagonal)/ length of left diagonal) (%) for the six positions. The algorithm error is of the
order of 0.3%. The method might be applied to other radiological systems without any major changes to adjust the
program code to other phantoms.
In this work a set of measurements for 54 CDMAM images, acquired in 11 different mammography systems from 6
manufacturers are presented. We can conclude that the distortion of all equipments is smaller than the recommendations
for maximum distortions in primary displays (2%)
The performance of 37 primary class liquid crystal display devices (2, 3 and 5 Mpixel matrix size) used in 9 different
diagnostic services in Spain has been determined in terms of 13 quantitative and visual evaluations. The equipment had
never been subjected to calibration or to QC tests since commissioning by vendors, between 2 and 18 months before
measurements. Tests, using calibrated luminance meters and TG18 patterns, have evaluated ambient light conditions and
other basic performance indicators, namely, display geometric distortion, artefacts, resolution and low-contrast visibility,
contrast luminance response compliance to DICOM standard, luminance extreme values and uniformity between pairs of
monitors associated to a same workstation. The principal sources of non-compliance are failures to visualize low-contrast
test objects (73% of displays), excessive differences with the DICOM contrast response standard (57%), and non-uniform
response of monitor pairs (54%). Also, 43% of LCD were found located in places with excessive illumination
and presenting specular reflections from faceplates. The analysis of ten 5 Mpixel displays, of possible use in
mammography services, indicates similar performance as the rest of monitors, except for the ambient luminance (100%
complying with recommendations) and larger non-compliance with the DICOM response standard (80%). No correlation
between image quality indicators and monitor hours of operation was found.