Predicting the fidelity of JPEG2000 compressed CT images using DICOM header information - Kim, Kil Joong; Kim, Bohyoung; Lee, Hyunna; Choi, Hosik; Jeon, Jong-June; Ahn, Jeong-Hwan; Lee, Kyoung Ho (Medical physics (Lancaster), 2011)
Keywords: ABDOMEN; ALGORITHMS; Biomedical modeling; CAT SCANNING; COMPRESSION RATIO; Computed tomography; Computerised tomographs; computerised tomography; CORRELATIONS; data compression; Data Compression - methods; diagnostic radiography; DICOM; Digital computing or data processing equipment or methods, specially adapted for specific applications; Digital radiography; Image analysis; image coding; Image coding, e.g. from bit‐mapped to non bit‐mapped; image compression; Image data processing or generation, in general; image fidelity; IMAGE PROCESSING; Image quality; Information Storage and Retrieval - methods; INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; LEARNING; machine learning algorithm; Medical image artifacts; Medical image noise; medical image processing; Medical image reconstruction; Medical imaging; MEDICINE; Neural information; neural nets; NEURAL NETWORKS; Neural Networks (Computer); PATIENTS; PHOTOGRAPHY; Radiographic Image Enhancement - methods; Radiographic Image Interpretation, Computer-Assisted - methods; Radiography, Abdominal - methods; Radiologists; RADIOLOGY AND NUCLEAR MEDICINE; REGRESSION ANALYSIS; Reproducibility of Results; SENSITIVITY ANALYSIS; Sensitivity and Specificity; Testing procedures; THICKNESS; Tomography, X-Ray Computed - methods; TRAINING; computed tomography; image fidelity; DICOM; machine learning algorithm; image compression