Effects of Varying Filter Kernel Sizes on the Image Quality of Interstitial Lung Diseases

Müller, R.-D.; Herting, D.; Hirche, H.; John, V.; Buddenbrock, B.; Gocke, Peter; Wiebringhaus, Robert; Braunschweig, R.; Voss, Michael; Mohnke, M.; Konietzko, N

doi:10.1177/02841851960373p262

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…Presently, many routine inspections and preoperative diagnosis require support of medical images, but negative factors in the imaging process, such as light scattering, electrical equipment noise as well as the complex body constitution and organization, result in broad dynamic range of medical images with rich details, poor contrast and indistinct margin details [1][2]. Most image enhancement algorithms regulate image contrast on the basis of characteristic center dimensions, but it is quite difficult to choose proper center dimensions [3]. This paper suggests a kind of self-adapted layer contrast enhancement algorithm, which could display details of medical images clearly without selecting characteristic center dimensions and it is good for clinical application without generating artifacts.…”

Section: Introductionmentioning

confidence: 99%

Self-Adapted Layer Contrast Enhancement Algorithm Based on Medical Image

Zhang

2014

AMM

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the paper suggests a kind of self-adapted layer contrast enhancement algorithm for medical images, which, in reference to Laplacian pyramid function, could compose images, and enhance pixel contrast on each layer through F/E processing and use of self-adaptation Sigmoid function, and it uses layer enhancement factor to control the enhancement degree. The result shows that details of medical images can be displayed clearly with this algorithm without selecting characteristic center dimensions. It will not generate artifacts and thus improves visual effect of medical images.

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Section: Introductionmentioning

confidence: 99%

Self-Adapted Layer Contrast Enhancement Algorithm Based on Medical Image

Zhang

2014

AMM

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Selection of Appropriate Computed Tomographic Image Reconstruction Algorithms for a Quantitative Multicenter Trial of Diffuse Lung Disease

Zhang

Bruesewitz

Bartholmai

et al. 2008

Journal of Computer Assisted Tomography

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Quantitative assessment of image quality enhancement due to unsharp-mask processing in x-ray fluoroscopy

Jabri

Wilson

2002

J. Opt. Soc. Am. A

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Spatial unsharp-mask processing and its variants are commonly used in x-ray radiography to enhance image contrast. We investigated the effect of three unsharp-masking filter kernels of different sizes on the detection of an advanced guidewire tip in simulated x-ray fluoroscopy image sequences. To isolate the effect of visual temporal processing, we repeated the experiments on single images. Filter gains were selected so that all three kernels increased the contrast of a 0.018-in. (0.457-mm) guidewire by a factor of 2 but had different effects on image noise and signal profiles. There was no statistically significant effect of unsharp masking on human-observer performance in single images. However, all three kernels significantly improved average performance in image sequences, and the guidewire contrast required for detection was reduced by 32%-40%. A prewhitening channelized observer model predicted the disparity between sequences and single images and fitted measurements at different kernel sizes well. A nonprewhitening observer model did not. We conclude that unsharp masking is a simple and effective method of improving guidewire visualization in fluoroscopically guided interventional procedures and that quantitative image quality studies are essential for evaluation of image-processing techniques in sequences such as x-ray fluoroscopy.

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Effects of Varying Filter Kernel Sizes on the Image Quality of Interstitial Lung Diseases

Abstract: Whenever optimized postprocessing is involved, storage phosphor radiography is equal to a modern screen-film system and can be substituted for the latter without any loss of image quality; this is especially valid for the imaging of interstitial infiltrates of the lung.

Cited by 3 publications

References 26 publications

Self-Adapted Layer Contrast Enhancement Algorithm Based on Medical Image

Self-Adapted Layer Contrast Enhancement Algorithm Based on Medical Image

Selection of Appropriate Computed Tomographic Image Reconstruction Algorithms for a Quantitative Multicenter Trial of Diffuse Lung Disease

Quantitative assessment of image quality enhancement due to unsharp-mask processing in x-ray fluoroscopy

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