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Development of an algorithm to automatically compress a CT image to visually lossless threshold

BACKGROUND: To develop an algorithm to predict the visually lossless thresholds (VLTs) of CT images solely using the original images by exploiting the image features and DICOM header information for JPEG2000 compression and to evaluate the algorithm in comparison with pre-existing image fidelity met...

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Autores principales: Nam, Chang-Mo, Lee, Kyong Joon, Ko, Yousun, Kim, Kil Joong, Kim, Bohyoung, Lee, Kyoung Ho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6297995/
https://www.ncbi.nlm.nih.gov/pubmed/30558555
http://dx.doi.org/10.1186/s12880-017-0244-2
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author Nam, Chang-Mo
Lee, Kyong Joon
Ko, Yousun
Kim, Kil Joong
Kim, Bohyoung
Lee, Kyoung Ho
author_facet Nam, Chang-Mo
Lee, Kyong Joon
Ko, Yousun
Kim, Kil Joong
Kim, Bohyoung
Lee, Kyoung Ho
author_sort Nam, Chang-Mo
collection PubMed
description BACKGROUND: To develop an algorithm to predict the visually lossless thresholds (VLTs) of CT images solely using the original images by exploiting the image features and DICOM header information for JPEG2000 compression and to evaluate the algorithm in comparison with pre-existing image fidelity metrics. METHODS: Five radiologists independently determined the VLT for 206 body CT images for JPEG2000 compression using QUEST procedure. The images were divided into training (n = 103) and testing (n = 103) sets. Using the training set, a multiple linear regression (MLR) model was constructed regarding the image features and DICOM header information as independent variables and regarding the VLTs determined with median value of the radiologists’ responses (VLT(rad)) as dependent variable, after determining an optimal subset of independent variables by backward stepwise selection in a cross-validation scheme. The performance was evaluated on the testing set by measuring absolute differences and intra-class correlation (ICC) coefficient between the VLT(rad) and the VLTs predicted by the model (VLT(model)). The performance of the model was also compared two metrics, peak signal-to-noise ratio (PSNR) and high-dynamic range visual difference predictor (HDRVDP). The time for computing VLTs between MLR model, PSNR, and HDRVDP were compared using the repeated ANOVA with a post-hoc analysis. P < 0.05 was considered to indicate a statistically significant difference. RESULTS: The means of absolute differences with the VLT(rad) were 0.58 (95% CI, 0.48, 0.67), 0.73 (0.61, 0.85), and 0.68 (0.58, 0.79), for the MLR model, PSNR, and HDRVDP, respectively, showing significant difference between them (p < 0.01). The ICC coefficients of MLR model, PSNR, and HDRVDP were 0.88 (95% CI, 0.81, 0.95), 0.85 (0.79, 0.91), and 0.84 (0.77, 0.91). The computing times for calculating VLT per image were 1.5 ± 0.1 s, 3.9 ± 0.3 s, and 68.2 ± 1.4 s, for MLR metric, PSNR, and HDRVDP, respectively. CONCLUSIONS: The proposed MLR model directly predicting the VLT of a given CT image showed competitive performance to those of image fidelity metrics with less computational expenses. The model would be promising to be used for adaptive compression of CT images.
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spelling pubmed-62979952018-12-19 Development of an algorithm to automatically compress a CT image to visually lossless threshold Nam, Chang-Mo Lee, Kyong Joon Ko, Yousun Kim, Kil Joong Kim, Bohyoung Lee, Kyoung Ho BMC Med Imaging Technical Advance BACKGROUND: To develop an algorithm to predict the visually lossless thresholds (VLTs) of CT images solely using the original images by exploiting the image features and DICOM header information for JPEG2000 compression and to evaluate the algorithm in comparison with pre-existing image fidelity metrics. METHODS: Five radiologists independently determined the VLT for 206 body CT images for JPEG2000 compression using QUEST procedure. The images were divided into training (n = 103) and testing (n = 103) sets. Using the training set, a multiple linear regression (MLR) model was constructed regarding the image features and DICOM header information as independent variables and regarding the VLTs determined with median value of the radiologists’ responses (VLT(rad)) as dependent variable, after determining an optimal subset of independent variables by backward stepwise selection in a cross-validation scheme. The performance was evaluated on the testing set by measuring absolute differences and intra-class correlation (ICC) coefficient between the VLT(rad) and the VLTs predicted by the model (VLT(model)). The performance of the model was also compared two metrics, peak signal-to-noise ratio (PSNR) and high-dynamic range visual difference predictor (HDRVDP). The time for computing VLTs between MLR model, PSNR, and HDRVDP were compared using the repeated ANOVA with a post-hoc analysis. P < 0.05 was considered to indicate a statistically significant difference. RESULTS: The means of absolute differences with the VLT(rad) were 0.58 (95% CI, 0.48, 0.67), 0.73 (0.61, 0.85), and 0.68 (0.58, 0.79), for the MLR model, PSNR, and HDRVDP, respectively, showing significant difference between them (p < 0.01). The ICC coefficients of MLR model, PSNR, and HDRVDP were 0.88 (95% CI, 0.81, 0.95), 0.85 (0.79, 0.91), and 0.84 (0.77, 0.91). The computing times for calculating VLT per image were 1.5 ± 0.1 s, 3.9 ± 0.3 s, and 68.2 ± 1.4 s, for MLR metric, PSNR, and HDRVDP, respectively. CONCLUSIONS: The proposed MLR model directly predicting the VLT of a given CT image showed competitive performance to those of image fidelity metrics with less computational expenses. The model would be promising to be used for adaptive compression of CT images. BioMed Central 2018-12-17 /pmc/articles/PMC6297995/ /pubmed/30558555 http://dx.doi.org/10.1186/s12880-017-0244-2 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Technical Advance
Nam, Chang-Mo
Lee, Kyong Joon
Ko, Yousun
Kim, Kil Joong
Kim, Bohyoung
Lee, Kyoung Ho
Development of an algorithm to automatically compress a CT image to visually lossless threshold
title Development of an algorithm to automatically compress a CT image to visually lossless threshold
title_full Development of an algorithm to automatically compress a CT image to visually lossless threshold
title_fullStr Development of an algorithm to automatically compress a CT image to visually lossless threshold
title_full_unstemmed Development of an algorithm to automatically compress a CT image to visually lossless threshold
title_short Development of an algorithm to automatically compress a CT image to visually lossless threshold
title_sort development of an algorithm to automatically compress a ct image to visually lossless threshold
topic Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6297995/
https://www.ncbi.nlm.nih.gov/pubmed/30558555
http://dx.doi.org/10.1186/s12880-017-0244-2
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