Current clinical image quality assessment techniques mainly analyze image quality for the imaging system in terms of
factors such as the capture system DQE and MTF, the exposure technique, and the particular image processing method
and processing parameters. However, when assessing a clinical image, radiologists seldom refer to these factors, but
rather examine several specific regions of the image to see whether the image is suitable for diagnosis. In this work, we
developed a new strategy to learn and simulate radiologists' evaluation process on actual clinical chest images. Based on
this strategy, a preliminary study was conducted on 254 digital chest radiographs (38 AP without grids, 35 AP with 6:1
ratio grids and 151 PA with 10:1 ratio grids). First, ten regional based perceptual qualities were summarized through an
observer study. Each quality was characterized in terms of a physical quantity measured from the image, and as a first
step, the three physical quantities in lung region were then implemented algorithmically. A pilot observer study was
performed to verify the correlation between image perceptual qualities and physical quantitative qualities. The results
demonstrated that our regional based metrics have promising performance for grading perceptual properties of chest
radiographs.
The quality of a digital radiograph for diagnostic imaging depends on many factors, such as the capture system DQE and
MTF, the exposure technique factors, the patient anatomy, and the particular image processing method and processing
parameters used. Therefore, the overall image quality as perceived by the radiologists depends on many factors. This
work explores objective image quality metrics directly from display-ready patient images. A preliminary study was
conducted based on a multi-frequency analysis of anatomy contrast and noise magnitude from 250 computed
radiography (CR) chest radiographs (150 PA, 50 AP captured with anti-scatter grids, and 50 AP without grids). The
contrast and noise values were evaluated in different sub-bands separately according to their frequency properties.
Contrast-Noise ratio (CNR) was calculated, the results correlated well with the human observers' overall impression on
the images captured with and without grids.
An observer study was conducted on a randomly selected sampling of 152 digital projection radiographs of varying
body parts obtained from four medical institutions for the purpose of assessing a new workflow-efficient imageprocessing
framework. Five rendering treatments were compared to measure the performance of a new processing
algorithm against the control condition. A key feature of the new image processing is the capability of processing without
specifying the exam. Randomized image pairs were presented at a softcopy workstation equipped with two diagnosticquality
flat-panel monitors. Five board-certified radiologists and one radiology resident independently reviewed each
image pair blinded to the specific processing used and provided a diagnostic-quality rating using a subjective rank-order
scale for each image. In addition, a relative preference rating was used to indicate rendering preference. Aggregate results
indicate that the new fully automated processing is preferred (sign test for median = 0 (α = 0.05): p < 0.0001 preference
in favor of the control).
An observer study was conducted to compare the diagnostic quality of human-subject images obtained using a-Se (amorphous selenium) and CsI(Tl) (thalium-doped cesium iodide) flat-panel detectors. Each detector was attached to an X-ray source and gantry equipment of similar configuration and was installed in a university hospital radiology department in X-ray rooms within close proximity. One hundred image pairs that represent a stratified sampling of exam types were acquired. For a particular subject, image pairs were captured of the same body part and projection, using each of the two detectors. The images comprising a pair were captured within a few minutes of each other. Using manual exposure methods, the images were captured with technique factors that correspond to average exposure levels equivalent to approximately a 400-speed screen-film system. Raw image data from both digital radiography systems was stored to a research workstation. To achieve images having the same appearance, the same image-processing software was used to render the data from both systems, although different parameters were used in the frequency processing to account for the different MTF and noise properties of the CsI(Tl) and a-Se detectors. The processed images were evaluated by radiologists who used a research workstation that was equipped with a 3 MP flat-panel monitor, and software to facilitate the image comparisons. Radiologists used subjective rank-order criteria to evaluate overall diagnostic quality and preference. Radiologists' ratings indicate that both detectors produce images that have comparable satisfactory diagnostic quality for images captured using exposure technique factors that correspond to a 400-speed screen-film system, but the CsI(Tl) detector produces significantly higher preference, especially for larger and denser exam types.
This paper presents an algorithm for segmentation of computed radiography (CR) images of extremities into bone and soft tissue regions. The algorithm is a region-based one in which the regions are constructed using a growing procedure with two different statistical tests. Following the growing process, tissue classification procedure is employed. The purpose of the classification is to label each region as either bone or soft tissue. This binary classification goal is achieved by using a voting procedure that consists of clustering of regions in each neighborhood system into two classes. The voting procedure provides a crucial compromise between local and global analysis of the image, which is necessary due to strong exposure variations seen on the imaging plate. Also, the existence of regions whose size is large enough such that exposure variations can be observed through them makes it necessary to use overlapping blocks during the classification. After the classification step, resulting bone and soft tissue regions are refined by fitting a 2nd order surface to each tissue, and reevaluating the label of each region according to the distance between the region and surfaces. The performance of the algorithm is tested on a variety of extremity images using manually segmented images as gold standard. The experiments showed that our algorithm provided a bone boundary with an average area overlap of 90% compared to the gold standard.
Antiscatter grids are commonly used in projection radiography to reduce scattered x-rays and improve image contrast and signal-to noise ratio. In digital radiography, because of spatial sampling, stationary girds usually cause aliasing and sometimes result in moire patterns. We investigated the impact of stationary grids in computed radiography (CR) for images viewed both on film and soft- copy display devices. First, the relationship of various grid factors as they relate to the problem of aliasing and moire artifacts is presented, as well as recommendations of grid usage with CR systems. Next, because ultimately one would like the freedom to use nay grid configuration with their digital imaging system, we present an automated image processing method to detect and adaptively suppress the grid to reduce aliasing artifacts.
Computed radiography is used for a wide range of projection radiography examinations. To produce useful diagnostic images it is necessary to apply an appropriate tone scale to the raw CR data. This paper presents a new automated tone scaling method for computed radiography and presents the results of a clinical study of the algorithm encompassing a wide range of clinical examinations.
An algorithm for the detection of the skin-line transitions in computed radiographic imagery is presented. Knowledge of the gray-level values associated with the skin-line transition can be utilized by a tone-scaling algorithm to prevent the loss of visibility of the skin line. Features associated with the line profile of the significant transitions are used to identify the kin-line transition in digital radiographs. A Gaussian maximum likelihood classifier is used to separate the skin-line transitions from those associated with the background-foreground, background-hardware, and other significant transitions. Results are presented for a set of operational computed radiography exams.
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