Scale-invariant feature transform based Lesion Detection on CT Lung Images

Volume: 2    Issue: 7

Year of Publication: 2016

Pages: (37-42)

Authors: Gurpreet Kaur, Navneet Kaur

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Abstract

As the image processing field grows day by day, researcher moves towards bio medical field to emerge new techniques and to diagnose various medical diseases using automated image processing algorithms. One of them is Lung lesion detection also known as Cancer Detection. Many researchers has worked on lung lesion detection. But successful interpretation mainly depends on the feature extraction, so it is very important and crucial step in lung lesion detection. So, this paper has proposed a unique method based on the combination of SIFT feature extraction method with two classification algorithms i.e. neural network and Support Vector Machines Algorithm. The proposed algorithm is based on 16 CT images for implementation of the algorithm. From result evaluation it has been seen that NN outperforms than SVM in terms of classification value.

References

1. Y. Song, W. Cai, S. Eberl, M. J. Fulham, and D. Feng, “Automatic detection of lung tumor and abnormal regional lymph nodes in PET-CT images,” J. Nucl. Med., vol. 52, no. Suppl. 1, p. 211, 2011.

2. H. Cui, X. Wang, and D. Feng, “Automated localization and segmentation of lung tumor from PET-CT thorax volumes based on image feature analysis,” in Proc. IEEE EMBC, 2012, pp. 5384–5387.

3. H. Ying, F. Zhou, A. F. Shields, O. Muzik, D. Wu, and E. I. Heath, “A novel computerized approach to enhancing lung tumor detection in whole-body PET images,” in Proc. IEEE EMBC, 2004, pp. 1589–1592.

4. C. Lartizien, S. Marache-Francisco, and R. Prost, “Automatic detection of lung and liver lesions in 3-D positron emission tomography images: A pilot study,” IEEE Trans. Nucl. Sci., vol. 59, no. 1, pp. 102–112, Feb. 2012.

5. Y. Song, W. Cai, H. Huang, X. Wang, S. Eberl, M. Fulham, and D.mFeng, “Similarity guided feature labeling for lesion detection,” in MICCAI. Heidelberg, Germany: Springer, 2013, vol. 8149, LNCS, pp. 284–291.

6. D.Hellwig, T. P.Graeter, D.Ukena, A. Groeschel, G. W. Sybrecht,H. J. Schaefers, and C.M. Kirsch, “18F-FDG PET for mediastinal staging of lung cancer:Which SUV thresholdmakes sense,” J. Nucl.Med., vol. 48, pp. 1761–1766, 2007.

7. C. Lartizien, M. Rogez, A. Susset, F. Giammarile, E. Niaf, and F. Ricard, “Computer aided staging of lymphoma patients with FDG PET/CT imaging based on textural information,” in Proc. Int. Symp. Biomed. Imag., 2012, pp. 118–121.

8. I.Jafar, H. Ying, A. Shields, and O. Muzik, “Computerized detection of lung tumors in PET/CT images,” in Proc. IEEE EMBC, 2006, pp. 2320–2323.

9. Y. Cui, B. Zhao, T. Akhurst, J. Yan, and L. Schwartz, “CT-guided automated detection of lung tumors on PET images,” in Proc. SPIE Med. Imag., 2008, vol. 6915, p. 69152N.

10. C. Ballangan, X.Wang, S. Eberl, M. Fulham, and D. Feng, “Automated detection and delineation of lung tumors in PET-CT volumes using a lung atlas and iterative mean-SUV threshold,” in Proc. SPIE Med. Imag., 2009, vol. 7259, p. 72593F.

11. J. Gubbi, A. Kanakatte, T. Kron, D. Binns, B. Srinivasan, N. Mani, and M. Palaniswami, “Automatic tumour volume delineation in respiratory- gated PET images,” J. Med. Imag. Radiat. Oncol., vol. 55, pp.65–76, 2011.

12. Yang Song, WeidongCai, Heng Huang, Xiaogang Wang, Yun Zhou, Michael J. Fulham, and David Dagan Feng, “Lesion Detection and Characterization With Context Driven Approximation in Thoracic FDG PET-CT Images of NSCLC Studies” ieee transactions on medical imaging, vol. 33, no. 2, february 2014.

13. Shanthi Mahesh, “A New Approach For An Improved Multiple Brain Lesion Segmentation”, International Journal Of Innovative Research In Computer And Communication Engineering (An Iso 3297: 2007 Certified Organization), Vol2, Issue.4, 2014.

14. Mohammad, “Tumor Brain Detection Through Mr Images: A Review Of Literature”, Journal Of Theoretical And Applied Information Technology, Pp.387-404, 2014.

15. U. Javed, M. M. Riaz, T. A. Cheema and H. M. F. Zafar., “Detection of Lung Tumor in CE CT Images byusing Weighted Support Vector Machines”, IEEE, pp. 113-116, 2013.

16. Donia Ben Hassen and HassenTaleb, “Lesion Detection in Lung Regions that are segmented Using Spatial Relations” 2012 International Conference on Information Technology and e-Services ©2012 IEEE.

17. http://docs.opencv.org/master/da/df5/tutorial_py_sift_intro.html.

18. www.support-vector-machines.org/

19. Z. Daniele, H. Andrew, J. Nickerson, \"Nuclear Structure in Cancer Cells,\" Nature Reviews Cancer, Medical School, vol. 4, no. 9, pp. 677-87, USA, Sep. 2004. (Pubitemid 39215066).

20. U. Javed, M. M. Riaz, T. A. Cheema and H. M. F. Zafar., “Detection of Lung Tumor in CE CT Images byusing Weighted Support Vector Machines”, IEEE, pp. 113-116, 2013.

21. V. V. Thakare, P. Singhal, “Neural network based CAD model for the design of rectangular patch antennas,” JETR, Vol. 2(7), 2010.

Keywords

Lesion Detection, SVM, NN, Segmentation, Classification, Feature extraction.