Temporal subtraction in chest radiography: Automated assessment of registration accuracy

Armato, Samuel G.; Doshi, Devang; Engelmann, Roger; Croteau, Charles L.; MacMahon, Heber

doi:10.1118/1.2184441

Cited by 16 publications

(9 citation statements)

References 11 publications

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“…These 69 patients were among the 72 patients whose images were used in our previous registration accuracy study. 15 The digital images of these patients were used for this study only if ͑1͒ at least two temporally sequential CR images were available and ͑2͒ a computed tomography ͑CT͒ scan acquired within 1 week of the "most recent" CR image was available to differentiate misregistration artifacts from pathologic change. All available CR images prior to and including the most recent image were collected for a total of 193 CR images from these 69 patients.…”

Section: Iia Patient Databasementioning

confidence: 99%

“…We have previously reported an automated method to identify temporal subtraction images that demonstrate suboptimal registration accuracy. 15 A database of 150 temporal subtraction images constructed from the computed radiography ͑CR͒ images of 72 patients was collected. The registration accuracy of these images was rated subjectively using a 5-point scale ranging from "5-excellent" to "1-poor;" ratings of 3, 4, or 5 reflected clinically acceptable subtraction images, and ratings of 1 or 2 reflected clinically unacceptable images.…”

Section: Introductionmentioning

confidence: 99%

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Temporal subtraction in chest radiography: Mutual information as a measure of image quality

et al. 2009

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Purpose: Temporal subtraction is used to detect the interval change in chest radiographs and aid radiologists in patient diagnosis. This method registers two temporally different images by geometrically warping the lung region, or "lung mask," of a previous radiographic image to align with the current image. The gray levels of every pixel in the current image are subtracted from the gray levels of the corresponding pixels in the warped previous image to form a temporal subtraction image. While temporal subtraction images effectively enhance areas of pathologic change, misregistration of the images can mislead radiologists by obscuring the interval change or by creating artifacts that mimic change. The purpose of this study was to investigate the utility of mutual information computed between two registered radiographic chest images as a metric for distinguishing between clinically acceptable and clinically unacceptable temporal subtraction images. Methods: A radiologist subjectively rated the image quality of 138 temporal subtraction images using a 1 ͑poor͒ to 5 ͑excellent͒ scale. To objectively assess the registration accuracy depicted in the temporal subtraction images, which is the main factor that affects the quality of these images, mutual information was computed on the two constituent registered images prior to their subtraction to generate a temporal subtraction image. Mutual information measures the joint entropy of the current image and the warped previous image, yielding a higher value when the gray levels of spatially matched pixels in each image are consistent. Mutual information values were correlated with the radiologist's subjective ratings. To improve this correlation, mutual information was computed from a spatially limited lung mask, which was cropped from the bottom by 10%-60%. Additionally, the number of gray-level values used in the joint entropy histogram was varied. The ability of mutual information to predict the clinical acceptability of a temporal subtraction image was evaluated through receiver operating characteristic ͑ROC͒ analysis. Results: The mean correlation coefficient obtained between mutual information computed on constituent images and the subjective rating of temporal subtraction image quality was 0.785. ROC analysis yielded an average A z value of 0.852 for the ability of mutual information to distinguish between temporal subtraction images of clinically acceptable and clinically unacceptable quality. Conclusions:The results of this study establish a relationship between mutual information and temporal subtraction registration accuracy and demonstrate the ability of mutual information to objectively indicate the presence of misregistration artifacts.

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Section: Iia Patient Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Temporal subtraction in chest radiography: Mutual information as a measure of image quality

et al. 2009

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“…Ishida et al 78 improved the quality of subtraction images of the chest through an iterative warping approach. Armato et al 82 later developed an automated approach to the evaluation of temporal subtraction image quality.…”

Section: Enhanced Visualizationmentioning

confidence: 99%

Anniversary Paper: Image processing and manipulation through the pages ofMedical Physics

Armato

Ginneken

2008

Medical Physics

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The language of radiology has gradually evolved from "the film" (the foundation of radiology since Wilhelm Roentgen's 1895 discovery of x-rays) to "the image," an electronic manifestation of a radiologic examination that exists within the bits and bytes of a computer. Rather than simply storing and displaying radiologic images in a static manner, the computational power of the computer may be used to enhance a radiologist's ability to visually extract information from the image through image processing and image manipulation algorithms. Image processing tools provide a broad spectrum of opportunities for image enhancement. Gray-level manipulations such as histogram equalization, spatial alterations such as geometric distortion correction, preprocessing operations such as edge enhancement, and enhanced radiography techniques such as temporal subtraction provide powerful methods to improve the diagnostic quality of an image or to enhance structures of interest within an image. Furthermore, these image processing algorithms provide the building blocks of more advanced computer vision methods. The prominent role of medical physicists and the AAPM in the advancement of medical image processing methods, and in the establishment of the "image" as the fundamental entity in radiology and radiation oncology, has been captured in 35 volumes of Medical Physics.

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“…The scale was expanded to allow a finer granularity of rating that ranged from 1.0 to 5.9, in increments of 0.1. 18 The 0.1 rating increment allowed the radiologists to express ratings that captured more subtle differences in registration accuracy while at the same time preserving the opportunity to evaluate the assigned ratings on the more coarse, discrete 5-point scale. Prior to rating the subtraction images, the radiologists were shown a sampling of the images so that they could appreciate the range of registration accuracies within the database.…”

Section: Radiologist Evaluationmentioning

confidence: 99%

“…To capture information from local image neighborhoods, ROIs of dimension 64ϫ 64 pixels were placed with 33% overlap across the lung mask region; ROI dimension and overlap ratio were determined empirically from an independent pilot database of conventional subtraction images reported earlier. 18 The number of ROIs placed within the lung mask regions of the conventional subtraction images, the soft-tissue subtraction images, and the hybrid subtraction images ranged from 18-113 ͑mean: 64͒, 25-117 ͑mean: 65͒, and 21-117 ͑mean: 65͒, respectively.…”

Section: Automated Analysis Of Temporal Subtraction Imagesmentioning

confidence: 99%

Temporal subtraction of dual-energy chest radiographs

et al. 2006

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Temporal subtraction and dual-energy imaging are two enhanced radiography techniques that are receiving increased attention in chest radiography. Temporal subtraction is an image processing technique that facilitates the visualization of pathologic change across serial chest radiographic images acquired from the same patient; dual-energy imaging exploits the differential relative attenuation of x-ray photons exhibited by soft-tissue and bony structures at different x-ray energies to generate a pair of images that accentuate those structures. Although temporal subtraction images provide a powerful mechanism for enhancing visualization of subtle change, misregistration artifacts in these images can mimic or obscure abnormalities. The purpose of this study was to evaluate whether dual-energy imaging could improve the quality of temporal subtraction images. Temporal subtraction images were generated from 100 pairs of temporally sequential standard radiographic chest images and from the corresponding 100 pairs of dual-energy, soft-tissue radiographic images. The registration accuracy demonstrated in the resulting temporal subtraction images was evaluated subjectively by two radiologists. The registration accuracy of the soft-tissue-based temporal subtraction images was rated superior to that of the conventional temporal subtraction images. Registration accuracy also was evaluated objectively through an automated method, which achieved an area-under-the-ROC-curve value of 0.92 in the distinction between temporal subtraction images that demonstrated clinically acceptable and clinically unacceptable registration accuracy. By combining dual-energy soft-tissue images with temporal subtraction, misregistration artifacts can be reduced and superior image quality can be obtained.

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Temporal subtraction in chest radiography: Automated assessment of registration accuracy

Cited by 16 publications

References 11 publications

Temporal subtraction in chest radiography: Mutual information as a measure of image quality

Temporal subtraction in chest radiography: Mutual information as a measure of image quality

Anniversary Paper: Image processing and manipulation through the pages ofMedical Physics

Temporal subtraction of dual-energy chest radiographs

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