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Computerized Lung Nodule Detection Using 3D Feature Extraction and Learning Based Algorithms

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Abstract

In this paper, a Computer Aided Detection (CAD) system based on three-dimensional (3D) feature extraction is introduced to detect lung nodules. First, eight directional search was applied in order to extract regions of interests (ROIs). Then, 3D feature extraction was performed which includes 3D connected component labeling, straightness calculation, thickness calculation, determining the middle slice, vertical and horizontal widths calculation, regularity calculation, and calculation of vertical and horizontal black pixel ratios. To make a decision for each ROI, feed forward neural networks (NN), support vector machines (SVM), naïve Bayes (NB) and logistic regression (LR) methods were used. These methods were trained and tested via k-fold cross validation, and results were compared. To test the performance of the proposed system, 11 cases, which were taken from Lung Image Database Consortium (LIDC) dataset, were used. ROC curves were given for all methods and 100% detection sensitivity was reached except naïve Bayes.

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References

  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.

    Article  Google Scholar 

  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. 30:71602–1617, 2003, doi:10.1118/1.1580485.

    Article  Google Scholar 

  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. 11:3258–266, 2004, doi:10.1016/S1076-6332(03)00729-3.

    Article  Google Scholar 

  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. 31:2383–388, 2007, doi:10.1378/chest.06-1261.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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, 6515–32, 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, 6512–28, 2007.

  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.

  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.

  13. Hounsfield, G. N., Computed medical imaging. Med. Phys. 7:283–290, 1980, doi:10.1118/1.594709.

    Article  Google Scholar 

  14. Ozekes, S., Osman, O., and Camurcu, A. Y., Mammographic Mass Detection Using A Mass Template. Korean J. Radiol. 6:3221–228, 2005.

    Article  Google Scholar 

  15. Ronse, C., and Devijver, P. A., Connected components in binary images: the detection problem, Research Studies Press. Wiley, NY, 1984.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    MATH  Google Scholar 

  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.

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Authors and Affiliations

  1. Istanbul Commerce University, Ragip Gumuspala Cad. No:84 34378 Eminonu, Istanbul, Turkey

    Serhat Ozekes

  2. Electronics and Telecommunications Engineering, Halic University, Siracevizler Cad. Sisli, Istanbul, Turkey

    Onur Osman

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  1. Serhat Ozekes
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  2. Onur Osman
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Correspondence to Serhat Ozekes.

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Ozekes, S., Osman, O. Computerized Lung Nodule Detection Using 3D Feature Extraction and Learning Based Algorithms. J Med Syst 34, 185–194 (2010). https://doi.org/10.1007/s10916-008-9230-0

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  • DOI: https://doi.org/10.1007/s10916-008-9230-0

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