For digital X-ray detectors, the need to control factory yield and cost invariably leads to the presence of some defective
pixels. Recently, a standard procedure was developed to identify such pixels for industrial applications. However, no
quality standards exist in medical or industrial imaging regarding the maximum allowable number and size of detector
defects. While the answer may be application specific, the minimum requirement for any defect specification is that the
diagnostic quality of the images be maintained. A more stringent criterion is to keep any changes in the images due to
defects below the visual threshold. Two highly sensitive image simulation and evaluation methods were employed to
specify the fraction of allowable defects as a function of defect cluster size in general radiography. First, the most critical
situation of the defect being located in the center of the disease feature was explored using image simulation tools and a
previously verified human observer model, incorporating a channelized Hotelling observer. Detectability index d' was
obtained as a function of defect cluster size for three different disease features on clinical lung and extremity
backgrounds. Second, four concentrations of defects of four different sizes were added to clinical images with subtle
disease features and then interpolated. Twenty observers evaluated the images against the original on a single display
using a 2-AFC method, which was highly sensitive to small changes in image detail. Based on a 50% just-noticeable
difference, the fraction of allowed defects was specified vs. cluster size.
It has been widely recognized that gain and offset corrections are essential for obtaining diagnostic image quality from flat-panel digital X-ray detectors. While such corrections are straightforward for detectors that are always powered and operate in a steady state, a new generation of battery-powered wireless detectors poses new challenges. Factors that need to be taken into account when optimizing the operation of such devices include image quality, battery life, robustness with respect to environmental variables and use patterns, and workflow, e.g., the readiness of the detector upon operator interaction, exposure lag, and image access time. Examples are given of the resolution to these problems for a new portable
35 × 43 cm2 X-ray detector. Multiple power states are required to extend battery life, including a low power state that simply supports wireless communication while the detector is not taking images. As a consequence, the detector encounters slightly different operating conditions during the X-ray exposure compared with the dark images that are taken for offset compensation. A new offset-correction algorithm was developed to compensate for such systematic differences, and its performance was evaluated in terms of image uniformity and noise.
A flexible software tool was developed that combines predictive models for detector noise and blur with image
simulation and an improved human observer model to predict the clinical task performance of existing and future
radiographic systems. The model starts with high-fidelity images from a database and mathematical models of common
disease features, which may be added to the images at desired contrast levels. These images are processed through the
entire imaging chain including capture, the detector, image processing, and hardcopy or softcopy display. The simulated
images and the viewing conditions are passed to a human observer model, which calculates the detectability index d' of
the signal (disease or target feature). The visual model incorporates a channelized Hotelling observer with a luminance-dependent
contrast sensitivity function and two types of internal visual system noise (intrinsic and image background-induced).
It was optimized based on three independent human observer studies of target detection, and is able to predict
d' over a wide range of viewing conditions, background complexities, and target spatial frequency content. A more
intuitive metric of system performance, Task-Specific Detective Efficiency (TSDE), is defined to indicate how much
detector improvements would translate to better radiologist performance. The TSDE is calculated as the squared ratio of
d' for a system with the actual detector and a hypothetical system containing an ideal detector. A low TSDE, e.g., 5% for
the detection of 0.1 mm microcalcifications in typical mammography systems, indicates that improvements in the
detector characteristics are likely to translate to better detection performance. The TSDE of lung nodule detection is as
high as 75% even with the detective quantum efficiency (DQE) of the detector not exceeding 24%. Applications of the
model to system optimizations for flat-panel detectors, in mammography and dual energy digital radiography, are
A four-alternative forced-choice experiment was carried out to examine the effect of 8-bit versus 10-bit grayscale resolution on the detection of subtle lung nodules on a medical grayscale liquid crystal display (LCD). Sets of four independent backgrounds from each of three regions were derived from a very low-noise X-ray acquisition of a chest-phantom with an amorphous selenium radiographic detector. Simulated nodules of fixed diameter (10 mm) and varying contrast were digitally added to the centers of selected background images. Subsequently, multifrequency image processing was performed to enhance the image structures, followed by a tonescaling procedure that resulted in pixel values being specified as p-values, according to DICOM Part 14: The Grayscale Display Function. To investigate the effect that grayscale resolution may have upon softcopy detectability, each set of four images in the experiment was quantized to both 8-bit and 10-bit resolution. The resulting images were displayed on a DICOM-calibrated LCD display supporting up to 10 bits of grayscale input. Twenty observers with imaging expertise performed the nodule detection task for which the signal and location were known exactly. Results from all readers, chest regions, and backgrounds were pooled, and statistical significance between fractions of correct responses between 8-bit and 10-bit resolution was tested. Experimental results do not demonstrate a statistically significant difference in the fraction of correct answers between these two input grayscale resolutions.
The color rendition ad hoc team of INCITS W1.1 is working to address issues related to color and tone reproduction for printed output and its perceptual impact on color image quality. The scope of the work includes accuracy of specified colors with an emphasis on memory colors, color gamut, and the effective use of tone levels, including issues related to contouring. The team has identified three sub-attributes of color rendition: 1) color quantization, defined as the ability to merge colors where needed; 2) color scale, defined as the ability to distinguish color where needed; and 3) color fidelity, defined as a balance of colorimetric accuracy, in cases where a reference exists, and pleasing overall color appearance. Visual definitions and descriptions of how these sub-attributes are perceived have been developed. The team is presently working to define measurement methods for the sub-attributes, with the focus in 2004 being on color fidelity. This presentation will review the definitions and appearance of the proposed sub-attributes and the progress toward developing test targets and associated measurement methods to quantify the color quantization sub-attribute. The remainder of the discussion will focus on the recent progress made in developing measurement methods for the color fidelity sub-attribute.
A four-alternative forced-choice experiment was conducted to investigate the relative impact of detector noise and anatomical structure on detection of subtle lung nodules. Sets of four independent backgrounds from each of three regions (heart, ribs, and lung field between the ribs) were derived from a very low-noise chest-phantom capture. Simulated nodules of varying contrast and fixed diameter (10 mm) were digitally added to the centers of selected background images. Subsequently, signal-dependent noise was introduced to simulate amorphous selenium radiographic detector performance at typical 80, 200, 400, 800, or higher speed class exposures. Series of four nodule contrasts each were empirically selected to yield comparable ranges of detectability index (d') for each background type and exposure level. Thirty-six observers with imaging expertise performed the nodule detection task, for which the signal and location were known exactly. Equally detectable nodule contrasts for each background type and exposure level were computed and their squares plotted against detector noise variance. The intercepts and slopes of the linear regressions increased in the order of lung, heart, and ribs, correlating with apparent anatomical structural complexity. The regression results imply that the effect of anatomical structure dominated that of capture device noise at clinically relevant exposures and beyond.
The color rendition ad hoc team of INCITS W1.1 is working to address issues related to color and tone reproduction for printed output and its perceptual impact on color image quality. The scope of the work includes accuracy of specified colors with emphasis on memory colors, color gamut, and the effective use of tone levels, including issues related to contouring. The team has identified three sub-attributes of color rendition: (1) color quantization -- defined as the ability to merge colors where needed, (2) color scale -- defined as the ability to distinguish color where needed, and (3) color fidelity -- defined as the ability to match colors. Visual definitions and descriptions of how these sub-attributes are perceived have been developed. The team is presently defining measurement methods for these, with the first of the sub-attributes considered being color quantization. More recently, the problem of measuring color fidelity has been undertaken. This presentation will briefly review the definitions and appearance of the proposed sub-attributes. The remainder of the discussion will focus on the progress to date of developing test targets and associated measurement methods to quantify the color quantization and color fidelity sub-attributes.
SC593: Characterization and Prediction of Image Quality
This course explains how to evaluate the quality of an image using numerical scales and physical standards; and how to predict the distribution of quality that would be produced by a pictorial imaging system under conditions of actual customer use. A framework is presented for conducting calibrated, extensible psychometric research so that results from different experiments can be rigorously integrated to construct predictive software using Monte Carlo simulations. Development of generalized objective metrics correlating with perceptual attributes based on psychometric data is discussed in detail and a number of examples of practical applications to product design are provided.