Computerized classification of suspicious regions in chest radiographs using subregion Hotelling observers

Medical Physics

Volumn 28, Issue 12, 2001, Pages 2403-2409

  Baydush, Alan H ^a   Catarious, David M ^a   Lo, Joseph Y ^a   Abbey, Craig K ^b   Floyd, Carey E ^a  

^a DUKE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b CEDARS SINAI MEDICAL CENTER   (United States)

Author keywords

Artificial neural networks; CAD; Classification; Image processing; Pulmonary nodules

Indexed keywords

CLASSIFICATION (OF INFORMATION); IMAGE CLASSIFICATION; MEAN SQUARE ERROR; RADIOGRAPHY; ROUTERS;

CHEST RADIOGRAPHS; FOLLOW UP; HOTELLING OBSERVERS; IMAGES PROCESSING; NOISE CORRELATION; PIXEL AREAS; PULMONARY NODULES; REGION-OF-INTEREST; REGIONS OF INTEREST; SIGNAL CORRELATION;

NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER ASSISTED DIAGNOSIS; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; LUNG NODULE; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; THORAX RADIOGRAPHY;

EID: 0035664510     PISSN: 00942405     EISSN: None     Source Type: Journal    
DOI: 10.1118/1.1420402     Document Type: Article

References (30)

1. Cancer statistics, 1999
2. Role of radiology for imaging and biopsy of solitary pulmonary nodules
3. Computer-aided diagnosis in chest radiology
4. Effect of a computer-aided diagnosis scheme on radiologists' performance in detection of lung nodules on radiographs
5. Pulmonary nodules: Computer-aided detection in digital chest images
6. Differentiation between nodules and end-on vessels using a convolution neural network architecture
7. Artificial convolution neural network for medical image pattern recognition
8. Artificial convolution neural network techniques and applications for lung nodule detection
9. Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks
10. Reduction of false positives in computerized detection of lung nodules in chest radiographs using artificial neural networks, discriminant analysis, and a rule-based scheme
11. Detection of lung nodules in digital chest radiographs using artificial neural networks: A pilot study
12. Development of an improved CAD scheme for automated detection of lung nodules in digital chest images
13. Artificial neural network analysis for nodule detection in digital chest radiographs
14. Computer-aided diagnosis: A neural-network-based approach to lung nodule detection
15. Automatic lung nodule detection using profile matching and back-propagation neural network techniques
16. Computer-aided diagnosis of pulmonary nodules: Results of a large-scale observer test
17. Psychophysical study to test the ability of Th Hotelling trace criterion to predict human-performance
18. Hotelling trace criterion and its correlation with human-observer performance
19. Channelized Hotelling and human observer correlation for lesion detection in hepatic SPECT imaging
20. A comparison of human observer LROC and numerical observer ROC for tumor detection in SPECT images
21. Channelized Hotelling and human observer correlation for lesion detection in hepatic SPECT imaging
22. Comparison of Hotelling observer models and human observers in defect detection from myocardial SPECT imaging
23. Linear discriminants and image quality
24. Model observers for assessment of image quality
25. Objective assessment of image quality. 2. Fisher information, Fourier crosstalk, and figures of merit for task-performance
26. Hotelling trace criterion as a figure of merit for the optimization of imaging-systems
27. Stabilized estimages of Hotelling-observer detection performance in patient-structured noise
28. Human vs model observers in anatomic backgrounds
29. Characteristics of regions suspicious for pulmonary nodules on chest radiographs
30. Development of a digital image database for chest radiographs with and without a lung nodule: Receiver operating characteristic analysis of radiologists' detection of pulmonary nodules