Use of Image Features in Predicting Visually Lossless Thresholds of JPEG2000 Compressed Body CT Images: Initial Trial
Autor: | Thomas Richter, Rafal Mantiuk, Bohyoung Kim, Heung Sik Kang, Kil Joong Kim, Kyoung Ho Lee |
---|---|
Rok vydání: | 2013 |
Předmět: |
Adult
Male Adolescent ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pilot Projects Data_CODINGANDINFORMATIONTHEORY Sensitivity and Specificity Visual complexity Young Adult Image Interpretation Computer-Assisted Humans Medicine Whole Body Imaging Radiology Nuclear Medicine and imaging Computer vision Aged Observer Variation Lossless compression Jpeg2000 compression business.industry Reproducibility of Results Signal Processing Computer-Assisted computer.file_format Middle Aged Data Compression Image Enhancement JPEG 2000 Female Artificial intelligence Tomography X-Ray Computed business computer Algorithms Energy (signal processing) |
Zdroj: | Radiology. 268:710-718 |
ISSN: | 1527-1315 0033-8419 |
Popis: | To test the image features that may be useful in predicting the visually lossless thresholds (VLTs) of body computed tomographic (CT) images for Joint Photographic Experts Group 2000 (JPEG2000) compression.The institutional review board approved this study, with a waiver of informed patient consent. One hundred body CT studies obtained in different patients by using five scanning protocols were obtained, and 100 images, each of which was selected from each of the 100 studies, were collected. Five radiologists independently determined the VLT of each image for JPEG2000 compression by using the QUEST algorithm. The 100 images were randomly divided into two data sets-the training set (50 images) and the testing set (50 images)-and the division was repeated 200 times. For each of the 200 divisions, a multiple linear regression model was constructed on a training set and tested on a testing set regarding each of five image features-standard deviation of image intensity, image entropy, relative percentage of low-frequency (LF) energy, variation in high-frequency (HF) energy, and visual complexity-as independent variables and considering the VLTs determined with the median value of the radiologists' responses as a dependent variable. The root mean square residual and intraclass correlation coefficient (ICC) for the 200 divisions between the VLTs predicted by the models and those determined by radiologists were compared between the models by using repeated-measures analysis of variance with post-hoc comparisons.Mean root-mean-square residuals for multiple linear regression models constructed with variation in HF energy (1.20 ± 0.10 [standard deviation]) and visual complexity (1.09 ± 0.07) were significantly lower than those for standard deviation of image intensity (1.65 ± 0.13), image entropy (1.63 ± 0.14), and relative percentage of LF energy (1.58 ± 0.12) (P.01). ICCs for variation in HF energy (0.64 ± 0.05) and visual complexity (0.71 ± 0.04) were significantly higher than those for standard deviation of image intensity (0.04 ± 0.02), image entropy (0.05 ± 0.02), and relative percentage of LF energy (0.20 ± 0.04) (P.01).Among the five tested image features, variation in HF energy and visual complexity were the most promising in predicting the VLTs of body CT images for JPEG2000 compression. |
Databáze: | OpenAIRE |
Externí odkaz: |