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Title: An image-based technique to assess the perceptual quality of clinical chest radiographs

Abstract

Purpose: Current clinical image quality assessment techniques mainly analyze image quality for the imaging system in terms of factors such as the capture system modulation transfer function, noise power spectrum, detective quantum efficiency, and the exposure technique. While these elements form the basic underlying components of image quality, when assessing a clinical image, radiologists seldom refer to these factors, but rather examine several specific regions of the displayed patient images, further impacted by a particular image processing method applied, to see whether the image is suitable for diagnosis. In this paper, the authors developed a novel strategy to simulate radiologists' perceptual evaluation process on actual clinical chest images. Methods: Ten regional based perceptual attributes of chest radiographs were determined through an observer study. Those included lung grey level, lung detail, lung noise, rib-lung contrast, rib sharpness, mediastinum detail, mediastinum noise, mediastinum alignment, subdiaphragm-lung contrast, and subdiaphragm area. Each attribute was characterized in terms of a physical quantity measured from the image algorithmically using an automated process. A pilot observer study was performed on 333 digital chest radiographs, which included 179 PA images with 10:1 ratio grids (set 1) and 154 AP images without grids (set 2), to ascertain the correlationmore » between image perceptual attributes and physical quantitative measurements. To determine the acceptable range of each perceptual attribute, a preliminary quality consistency range was defined based on the preferred 80% of images in set 1. Mean value difference ({mu}{sub 1}-{mu}{sub 2}) and variance ratio ({sigma}{sub 1}{sup 2}/{sigma}{sub 2}{sup 2}) were investigated to further quantify the differences between the selected two image sets. Results: The pilot observer study demonstrated that our regional based physical quantity metrics of chest radiographs correlated very well with their corresponding perceptual attributes. The distribution comparisons, mean value difference estimations, and variance ratio estimations of each physical quantity between sets of images from two different techniques matched our expectation that the image quality of set 1 should be better than that of set 2. Conclusions: The measured physical quantities provide a robust reflection of perceptual image quality in clinical images. The methodology can be readily applied for automated evaluation of perceptual image quality in clinical chest radiographs.« less

Authors:
; ; ; ; ; ; ; ;  [1];  [2];  [2];  [2];  [2]
  1. Carl E. Ravin Advanced Imaging Laboratories, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705 (United States)
  2. (United States)
Publication Date:
OSTI Identifier:
22099090
Resource Type:
Journal Article
Journal Name:
Medical Physics
Additional Journal Information:
Journal Volume: 39; Journal Issue: 11; Other Information: (c) 2012 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-2405
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; 60 APPLIED LIFE SCIENCES; BIOMEDICAL RADIOGRAPHY; DIAGNOSIS; DISTRIBUTION; IMAGE PROCESSING; IMAGES; LUNGS; MEDIASTINUM; METRICS; MODULATION; NOISE; PATIENTS; QUANTUM EFFICIENCY; TRANSFER FUNCTIONS

Citation Formats

Lin Yuan, Luo Hui, Dobbins, James T. III, Page McAdams, H., Wang, Xiaohui, Sehnert, William J., Barski, Lori, Foos, David H., Samei, Ehsan, Carestream Health, Inc., 1049 Ridge Road West, Rochester, New York 14615, Carl E. Ravin Advanced Imaging Laboratories, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705, Carestream Health, Inc., 1049 Ridge Road West, Rochester, New York 14615, and Carl E. Ravin Advanced Imaging Laboratories, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705. An image-based technique to assess the perceptual quality of clinical chest radiographs. United States: N. p., 2012. Web. doi:10.1118/1.4760886.
Lin Yuan, Luo Hui, Dobbins, James T. III, Page McAdams, H., Wang, Xiaohui, Sehnert, William J., Barski, Lori, Foos, David H., Samei, Ehsan, Carestream Health, Inc., 1049 Ridge Road West, Rochester, New York 14615, Carl E. Ravin Advanced Imaging Laboratories, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705, Carestream Health, Inc., 1049 Ridge Road West, Rochester, New York 14615, & Carl E. Ravin Advanced Imaging Laboratories, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705. An image-based technique to assess the perceptual quality of clinical chest radiographs. United States. doi:10.1118/1.4760886.
Lin Yuan, Luo Hui, Dobbins, James T. III, Page McAdams, H., Wang, Xiaohui, Sehnert, William J., Barski, Lori, Foos, David H., Samei, Ehsan, Carestream Health, Inc., 1049 Ridge Road West, Rochester, New York 14615, Carl E. Ravin Advanced Imaging Laboratories, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705, Carestream Health, Inc., 1049 Ridge Road West, Rochester, New York 14615, and Carl E. Ravin Advanced Imaging Laboratories, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705. Thu . "An image-based technique to assess the perceptual quality of clinical chest radiographs". United States. doi:10.1118/1.4760886.
@article{osti_22099090,
title = {An image-based technique to assess the perceptual quality of clinical chest radiographs},
author = {Lin Yuan and Luo Hui and Dobbins, James T. III and Page McAdams, H. and Wang, Xiaohui and Sehnert, William J. and Barski, Lori and Foos, David H. and Samei, Ehsan and Carestream Health, Inc., 1049 Ridge Road West, Rochester, New York 14615 and Carl E. Ravin Advanced Imaging Laboratories, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705 and Carestream Health, Inc., 1049 Ridge Road West, Rochester, New York 14615 and Carl E. Ravin Advanced Imaging Laboratories, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705},
abstractNote = {Purpose: Current clinical image quality assessment techniques mainly analyze image quality for the imaging system in terms of factors such as the capture system modulation transfer function, noise power spectrum, detective quantum efficiency, and the exposure technique. While these elements form the basic underlying components of image quality, when assessing a clinical image, radiologists seldom refer to these factors, but rather examine several specific regions of the displayed patient images, further impacted by a particular image processing method applied, to see whether the image is suitable for diagnosis. In this paper, the authors developed a novel strategy to simulate radiologists' perceptual evaluation process on actual clinical chest images. Methods: Ten regional based perceptual attributes of chest radiographs were determined through an observer study. Those included lung grey level, lung detail, lung noise, rib-lung contrast, rib sharpness, mediastinum detail, mediastinum noise, mediastinum alignment, subdiaphragm-lung contrast, and subdiaphragm area. Each attribute was characterized in terms of a physical quantity measured from the image algorithmically using an automated process. A pilot observer study was performed on 333 digital chest radiographs, which included 179 PA images with 10:1 ratio grids (set 1) and 154 AP images without grids (set 2), to ascertain the correlation between image perceptual attributes and physical quantitative measurements. To determine the acceptable range of each perceptual attribute, a preliminary quality consistency range was defined based on the preferred 80% of images in set 1. Mean value difference ({mu}{sub 1}-{mu}{sub 2}) and variance ratio ({sigma}{sub 1}{sup 2}/{sigma}{sub 2}{sup 2}) were investigated to further quantify the differences between the selected two image sets. Results: The pilot observer study demonstrated that our regional based physical quantity metrics of chest radiographs correlated very well with their corresponding perceptual attributes. The distribution comparisons, mean value difference estimations, and variance ratio estimations of each physical quantity between sets of images from two different techniques matched our expectation that the image quality of set 1 should be better than that of set 2. Conclusions: The measured physical quantities provide a robust reflection of perceptual image quality in clinical images. The methodology can be readily applied for automated evaluation of perceptual image quality in clinical chest radiographs.},
doi = {10.1118/1.4760886},
journal = {Medical Physics},
issn = {0094-2405},
number = 11,
volume = 39,
place = {United States},
year = {2012},
month = {11}
}