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

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dc.contributor.authorNam, Chang-Mo-
dc.contributor.authorLee, Kyong Joon-
dc.contributor.authorKo, Yousun-
dc.contributor.authorKim, Kil Joong-
dc.contributor.authorKim, Bohyoung-
dc.contributor.authorLee, Kyoung Ho-
dc.date.accessioned2019-03-13T01:39:22Z-
dc.date.available2019-03-13T10:42:56Z-
dc.date.issued2018-12-17-
dc.identifier.citationBMC Medical Imaging, 18(1):53ko_KR
dc.identifier.issn1471-2342-
dc.identifier.urihttps://hdl.handle.net/10371/147068-
dc.description.abstractBackground
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 (VLTrad) 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 VLTrad and the VLTs predicted by the model (VLTmodel). 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 VLTrad 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 ± 0.1s, 3.9 ± 0.3s, and 68.2 ± 1.4s, 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.
ko_KR
dc.description.sponsorshipThis research was supported by grants of the Korean Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (No. HI13C0004 and HI15C1052), and Seoul National University Bundang Hospital Research Fund (No. 02-2015-056), and the Interdisciplinary Research Initiatives Program from College of Engineering and College of Medicine, Seoul National University (No. 800-20170169).ko_KR
dc.language.isoenko_KR
dc.publisherBioMed Centralko_KR
dc.subjectVisually lossless thresholdko_KR
dc.subjectCT compressionko_KR
dc.subjectDICOM headerko_KR
dc.titleDevelopment of an algorithm to automatically compress a CT image to visually lossless thresholdko_KR
dc.typeArticleko_KR
dc.contributor.AlternativeAuthor남창모-
dc.contributor.AlternativeAuthor이경준-
dc.contributor.AlternativeAuthor고유순-
dc.contributor.AlternativeAuthor김길중-
dc.contributor.AlternativeAuthor김보형-
dc.contributor.AlternativeAuthor이경호-
dc.identifier.doi10.1186/s12880-017-0244-2-
dc.language.rfc3066en-
dc.rights.holderThe Author(s).-
dc.date.updated2018-12-23T04:15:32Z-
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