Abstract
BackgroundTo 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.MethodsFive 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.ResultsThe 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 ± 0.1 s, 3.9 ± 0.3 s, and 68.2 ± 1.4 s, for MLR metric, PSNR, and HDRVDP, respectively.ConclusionsThe 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.
Highlights
To develop an algorithm to predict the visually lossless thresholds (VLTs) of computed tomography (CT) images solely using the original images by exploiting the image features and Digital Imaging and Communications in Medicine (DICOM) header information for JPEG2000 compression and to evaluate the algorithm in comparison with pre-existing image fidelity metrics
This study aimed to develop a computerized algorithm to predict the VLTs of CT images solely using the original images by exploiting the image features and DICOM header information for Joint Photographic Experts Group 2000 (JPEG2000) compression and to evaluate the performance of the algorithm in comparison with pre-existing image fidelity metrics
VLT measured by radiologists The intra-class correlation (ICC) coefficient between the five readers was 0.56
Summary
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. Regarding the estimation of such optimal compression level, many researchers have advocated that visually lossless threshold (VLT) is robust and conservative sufficiently to be adopted for the compression of medical images [6,7,8,9,10,11]. This approach focuses on image fidelity (i.e. the visual equivalence between the original and compressed images). The underlying idea of this approach is that if a compressed image is visually indistinguishable from its original, the artifacts should not affect the diagnosis
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