Computerized lung nodule detection using 3D Feature extraction and learning based algorithms

Journal of Medical Systems

Volumn 34, Issue 2, 2010, Pages 185-194

  Ozekes, Serhat ^a   Osman, Onur ^b  

^a ISTANBUL COMMERCE UNIVERSITY   (Turkey)

^b HALIC UNIVERSITY   (Turkey)

Author keywords

3D feature extraction; Feed forward neural networks; Logistic regression; Na ve Bayes; Support vector machines

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYESIAN LEARNING; CLASSIFICATION ALGORITHM; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; DATA BASE; DATA EXTRACTION; INTERMETHOD COMPARISON; LOGISTIC REGRESSION ANALYSIS; LUNG NODULE; SENSITIVITY ANALYSIS; SUPPORT VECTOR MACHINE; THREE DIMENSIONAL IMAGING;

ALGORITHMS; BAYES THEOREM; FALSE NEGATIVE REACTIONS; FALSE POSITIVE REACTIONS; HUMANS; LOGISTIC MODELS; LUNG NEOPLASMS; NEURAL NETWORKS (COMPUTER); RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; ROC CURVE; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, X-RAY COMPUTED;

EID: 77950459958     PISSN: 01485598     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10916-008-9230-0     Document Type: Article

References (22)

1. Greenlee, R. T., Nurray, T., Bolden, S., and Wingo, P. A., Cancer statistics 2000. CA Cancer J. Clin. 50:7-33, 2000, doi:10.3322/ canjclin.50.1.7.
2. Suzuki, K., Armato, S. G. 3rd, Li, F., Sone, S., and Doi, K., Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. Med. Phys. 30:71602-71617, 2003, doi:10.1118/1.1580485. (Pubitemid 36885778)
3. McCulloch, C. C., Kaucic, R. A., Mendonça, P. R., Walter, D. J., and Avila, R. S., Model-based detection of lung nodules in computed tomography exams. Acad. Radiol. 11:3258-3266, 2004, doi:10.1016/S1076-6332(03)00729-3. (Pubitemid 38269463)
4. Gould, M. K., Ananth, L., and Barnett, P. G., A clinical model to estimate the pretest probability of lung cancer in patients with solitary pulmonary nodules. Chest. 31:2383-2388, 2007, doi:10.1378/chest.06-1261. (Pubitemid 46280396)
5. Xu, X. W., Doi, K., Kobayashi, T., MacMahon, H., and Giger, M., Development of an improved CAD scheme for automated detection of lung nodules in digital chest images. Med. Phys. 24:1395-1403, 1997, doi:10.1118/1.598028. (Pubitemid 27415070)
6. Yim, Y., Hong, H., Shin, Y. G., Hybrid lung segmentation in chest CT images for computer-aided diagnosis. HEALTHCOM. 378-383, 2005.
7. Osman, O., Ozekes, S., and Ucan, O. N., Lung nodule diagnosis using 3D template matching. Comput. Biol. Med. 37:1167-1172, 2007, doi:10.1016/j. compbiomed.2006.10.007. (Pubitemid 46992797)
8. Korfiatis, P., Kalogeropoulou, C., Costaridou, L., Computer Aided Detection of Lung Nodules in Multislice Computed Tomography. ITAB, 2006.
9. Opfer, R., Wiemker, R.d, Performance Analysis For Computer-Aided Lung Nodule Detection On LIDC Data. Proc. SPIE, 6515-6532, 2007.
10. Ochs, R. A., Goldin, J. G., Abtin, F., Kim, H. J., Brown, K., Batra, P., Roback, D., Mcnitt-Gray, M. F., Brown, M. S., Multiscale Shape Features For Classification Of Bronchovascular Anatomy In CT Using Adaboost. Proc. SPIE, 6512-6528, 2007. (Pubitemid 350126936)
11. Sahiner, B., Hadjiiski, L. M., Chan, H. P., Shi, J., Way, T., Cascade, P. N., Kazerooni, E. A., Zhou, C., Wei, J., The Effect Of Nodule Segmentation On The Accuracy Of Computerized Lung Nodule Detection On CT Scans: Comparison On A Data Set Annotated By Multiple Radiologists. Proc. SPIE 6514:65140L, 2007, doi:10.1117/12.713771. (Pubitemid 47567543)
12. Samuel, G., Armato III., Geoffrey M., Michael F., Charles R., David Y., Lung image database consortium\Developing a resource for the medical imaging research community. Radiology, 739-748, 2004. (Pubitemid 39100258)
13. Hounsfield, G. N., Computed medical imaging. Med. Phys. 7:283-290, 1980, doi:10.1118/1.594709.
14. Ozekes, S., Osman, O., and Camurcu, A. Y., Mammographic Mass Detection Using A Mass Template. Korean J. Radiol. 6:3221-3228, 2005.
15. Ronse, C., and Devijver, P. A., Connected components in binary images: the detection problem, Research Studies Press. Wiley, NY, 1984.
16. Manohar, M., and Ramapriyan, H. K., Connected Component Labeling of Binary Images on a Mesh Connected Massively Parallel Processor. Comput. Vis. Graph. Image Process. 45:133-149, 1989, doi:10.1016/0734-189X(89)90129-1.
17. Stefano, L. D., Bulgarelli, A., A simple and efficient connected components labeling algorithm. In Proceedings of International Conference on Image Analysis and Processing. 322-327, 1999.
18. Boser, B. E., Guyon, I., Vapnik, V., A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory. 144-152, ACM, 1992.
19. Cortes, C., and Vapnik, V., Support-vector network. Mach. Learn. 20:273-297, 1995.
20. Chang C. C., Lin C. J., LIBSVM., A library for support vector machines. 2008.
21. Martínez, M., Sucar, L. E., Learning an optimal naïve Bayes classifier. In Proc. IEEE Inter. Conf. on Pattern Recognition (ICPR), China, 2006.
22. Stacey, A., Kildea, D., Genetic Algorithm search for large Logistic Regression models with significant variables. In proceeedings of the 22nd International Conference on Information Technology Interfaces: Croatia, Pula, 2000.