The development and evaluation of a computerized diagnosis scheme for pneumoconiosis on digital chest radiographs

BioMedical Engineering Online

Volumn 13, Issue 1, 2014, Pages

  Zhu, Biyun ^a   Luo, Wei ^a   Li, Baoping ^b   Chen, Budong ^c   Yang, Qiuying ^a   Xu, Yan ^c   Wu, Xiaohua ^c   Chen, Hui ^a   Zhang, Kuan ^a  

^a CAPITAL MEDICAL UNIVERSITY   (China)

^b China   (China)

^c CAPITAL MEDICAL UNIVERSITY   (China)

Author keywords

Bootstrap resampling; Digital radiograph; Pneumoconiosis; Support vector machine; Texture feature

Indexed keywords

COMPUTER AIDED DIAGNOSIS; DATA MINING; DECISION TREES; RADIOGRAPHY; SUPPORT VECTOR MACHINES; TREES (MATHEMATICS);

BOOTSTRAP RESAMPLING; CLASSIFICATION PERFORMANCE; COMPUTER AIDED DIAGNOSIS SYSTEMS; DIGITAL RADIOGRAPH; MORPHOLOGICAL RECONSTRUCTION; PNEUMOCONIOSIS; RECEIVER OPERATING CHARACTERISTIC CURVES; TEXTURE FEATURES;

CLASSIFICATION (OF INFORMATION);

ADULT; AREA UNDER THE CURVE; ARTICLE; BOOTSTRAPPING; CLASSIFICATION ALGORITHM; CLASSIFIER; CLINICAL ARTICLE; COMPUTER ASSISTED DIAGNOSIS; CONTROLLED STUDY; DECISION TREE; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DIGITAL RADIOGRAPHY; HUMAN; IMAGE RECONSTRUCTION; MALE; MIDDLE AGED; PNEUMOCONIOSIS; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; THORAX RADIOGRAPHY; AGED; ALGORITHM; AUTOMATED PATTERN RECOGNITION; CASE CONTROL STUDY; LUNG; PATHOLOGY; PROCEDURES; RADIOGRAPHY; STATISTICAL MODEL;

AGED; ALGORITHMS; AREA UNDER CURVE; CASE-CONTROL STUDIES; DECISION TREES; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; LINEAR MODELS; LUNG; MALE; MIDDLE AGED; PATTERN RECOGNITION, AUTOMATED; PNEUMOCONIOSIS; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; RADIOGRAPHY, THORACIC; SUPPORT VECTOR MACHINE;

EID: 84988624800     PISSN: None     EISSN: 1475925X     Source Type: Journal    
DOI: 10.1186/1475-925X-13-141     Document Type: Article

References (34)

1. Chong S, Lee KS, Chun MJ, Han J, Kwon OJ, Kim TS: Pneumoconiosis: comparison of imaging and pathologic findings. Radiographics 2006, 26:59-77.
2. International Labor Organization (ILO): Guidelines for the Use of the ILO International Classification of Radiographs of Pneumoconiosis. Occupational Safety and Health Series, No. 22 (Rev.). Geneva Switzerland: International Labor Office; 1980.
3. Savol AM, Li CC, Hoy RJ: Computer-aided recognition of small rounded pneumoconiosis opacities in chest X-rays. IEEE Trans Pattern Anal Mach Intell 1980, 2:479-482.
4. Yu P, Xu H, Zhu Y, Yang C, Sun X, Zhao J: An automatic computer-aided detection scheme for pneumoconiosis on digital chest radiographs. J Digit Imaging 2011, 24:382-393.
5. Yu P, Zhao J, Xu H, Yang C, Sun X, Chen S: Computer Aided Detection for Pneumoconiosis based on Histogram Analysis. In Proceedings of the 1st International Conference on Information Science and Engineering (ICISE). Nanjing, China: IEEE; 2009.
6. Yu P, Zhao J, Xu H, Sun X, Mao L: Computer Aided Detection for Pneumoconiosis based on Co-Occurrence Matrices Analysis. In Proceedings of the Second International Conference on Biomedical Engineering and Informatics. Tianjin, China: IEEE; 2011.
7. Katsuragawa S, Doi K, MacMahon H, Monnier-Cholley L, Morishita J, Ishida T: Quantitative analysis of geometric-pattern features of interstitial infiltrates in digital chest radiographs: preliminary results. J Digit Imaging 1996, 9:137-144.
8. Mir AH, Hanmandlu M, Tandon SN: Texture analysis of CT images. IEEE Eng Med Biol 1995, 14:781-786.
9. Zhu Y, Tan YQ, Hua YQ, Wang MP, Zhang G, Zhang JG: Feature selection and performance evaluation of support vector machine (SVM)-based classifier for differentiation benign and malignant pulmonary nodules by computed tomography. J Digit Imaging 2010, 23:51-65.
10. Yuan QF: Cancer Diagnosis by Using Support Vector Machine. MD Thesis. Chongqing University; 2007.
11. Lu Y: Automatic topic identification of health-related messages in online health community using text classification. Springer plus 2013, 2:309.
12. Zhu BY, Chen H: Morphological reconstruction based segmentation of lung fields on digital radiographs. Adv Mater Res 2013, 605:2155-2159.
13. Hering KG, Jacobsen M, Bosch-Galetke E, Elliehausen HJ, Hieckel HG, Hofmann-Preiss K, Jacques W, Jeremie U, Kotschy-Lang N, Kraus T, Menze B, Raab W, Raithel HJ, Schneider WD, Strassburger K, Tuengerthal S, Woitowitz HJ: Further development of the International Pneumoconiosis Classification-from ILO 1980 to ILO 2000 and to ILO 2000/German Federal Republic version. Pneumologie (Stuttgart, Germany) 2003, 57:576-584.
14. Arivazhagan S, Ganesan L: Texture segmentation using wavelet transform. Pattern Recogn Lett 2003, 24:3197-3203.
15. Kociolek M, Materka A, Strzelecki M, Szczypinski P: Discrete Wavelet Transform-Derived Features for Digital Image Texture Analysis. In Proceedings of International Conference on Signals and Electronic Systems. Lodz, Poland: IEEE; 2001.
16. Wu PC, Chen LG: An efficient architecture for two-dimensional discrete wavelet transform. IEEE Trans Circuits and Syst Video Tech 2001, 11:536-545.
17. Fukuda S, Hirosawa N: Land Cover Classification from Multi-Frequency Polarmetric Synthetic Aperture Radar Data using Wavelet-based Texture Information. In Proceedings of IEEE International Geoscience and Remote Sensing Symposium: 6-10 July 1998. Seattle, WA: IEEE; 1998:357-359.
18. Zhu BY, Chen H, Chen BD, Xu Y, Zhang K: Support vector machine model for diagnosis pneumoconiosis based on wavelet texture features of digital chest radiographs. J Digit Imaging 2014, 27:90-97.
19. Kira K, Rendell LA: The Feature Selection Problem: Traditional Methods and a New Algorithm. In Proceedings of the tenth National Conference on Artificial Intelligence: 12-16 July 1992. San Jose, CA: AAAI Press; 1992:129-134.
20. Bian ZQ, Zhang XG: Pattern Recognition 2nd ed. Beijing: Tsinghua University Publisher; 2000.
21. Quinlan JR: Induction Decision Tree. Mach Learn 1986, 1:81-106.
22. Li C, Zhi X, Ma J, Cui Z, Zhu Z, Zhang C: Performance comparison between logistic regression, decision trees, and multilayer perception in predicting peripheral neuropathy in type 2 diabetes mellitus. Chin Med J (Engl) 2012, 125:851-857.
23. Foster KR, Koprowski R, Skufca JD: Machine learning, medical diagnosis, and biomedical engineering research commentary. BioMed Eng OnLine 2014, 13:94-103.
24. Furkan K, Alexander S, Kivance K, Tulin E, Enis CA, Rengul C: Image classification of human carcinoma cells using complex wavelet-based covariance descriptors. PLoS One 2013, 8:e52807.
25. Fawcett T: An introduction to ROC analysis. Pattern Recogn Lett 2006, 27:861-874.
26. Efron B, Tibshirani R: An Introduction to the Bootstrap. New York: Chapman & Hall; 1993.
27. Sahiner B, Chan HP, Hadjiiski L: Classifier performance prediction for computer-aided diagnosis using a limited dataset. Med Phys 2008, 35:1559-1570.
28. Xu H, Tao X, Sundararajan R: Computer Aided Detection for Pneumoconiosis Screening on Digital Chest Radiographs. In Proceedings of the Third International Workshop on Pulmonary Image Analysis, September 20, 2010. Beijing, China: CreateSpace Independent Publishing Platform; 2010:129-138.
29. Okumura E, Kawashita I, Ishida T: Computerized analysis of pneumoconiosis in digital chest radiography: effect of artificial neural network trained with power spectra. J Digit Imaging 2011, 24:1126-1132.
30. Cai CX, Zhu BY, Chen H: Computer-aided diagnosis for pneumoconiosis based on texture analysis on digital chest radiographs. Appl Mech Mater 2013, 241-244:244-247.
31. Kondo K, Zhao B, Mino M: Automated Quantitative Analysis for Pneumoconiosis. In Proceedings of International Symposium on Multispectral Image Processing: 21-23 October 1998. Wuhan, China: SPIE; 1998.
32. Cohen R, Velho V: Update on respiratory disease from coal mine and silica dust. Clin Chest Med 2002, 23:811-826.
33. Cohen RA, Patel A, Green FH: Lung disease caused by exposure to coal mine and silica dust. Semin Respir Crit Care Med 2008, 29:651-661.
34. Castranova V, Vallyathan V: Silicosis and coal workers' pneumoconiosis. Environ Health Perspect 2000, 108(Suppl 4):675-684.