Summary of Results

An automated lung segmentation scheme is presented in this research paper. The technique is applied on 15 CT scans composed of 2920 slices. The method proposed was ableto successfully segment 2757 out 2920 slices which means a success rate of 94.42%.As to the best of our knowledge, this is the first attempt to segment lungs with disjoint therefore in terms of accuracy no comparison can be provided.

The lung segmentation results through our approach are evaluated against manual segmentation results of lungs done by radiologist. Figure 7 depicts the consequences of manual segmentation. The results indicate that our segmentation results and the one made by radiologist are similar. The scheme is unsuccessful in cases where trachea or airways are joined with either of the lungs. It is not possible to identify the trachea and bronchi which results in wrong segmentation of lungs.

Figure 7: Manual segmentation results

In this paper we develop a totally automatic technique to segment lungs from Computed Tomography (CT) scan slices. For initial segmentation we used Otsu algorithm and Connected Component Analysis algorithm. To further improve the segmentation and avoid over and under-segmentation morphological operations along with bitwise logical AND, OR operations are applied, which gives good results. The use of Otsu thresholding also helped overcome the difficulty in segmenting the conjoint of right and left lungs. Most of the segmentation techniques available in literature use predetermined threshold value to guide the whole segmentation process which effects the overall efficiency therefore we use a technique which is adaptive. In rare cases, due to overlap of anatomical structures there can be a disjoint in lungs. Solution to this special case is presented. This is solved by calculating the centroid of regions in the image. Up till now, no lung segmentation scheme has addressed the matter of disjoint in lungs which is a rare occurrence but is important. Hence, our technique is the first in this regard.

The proposed algorithm is applied to the database provided by Cornel University, USA which contains 15 CT scans with 2920 slices. We were able to successfully segment 2757 slices out 2920 slices which means a success rate of 94.42%. This is a very good success rate as the technique is applied to a very large number of slices.

Acknowledgments

1. This work was fully funded by the Deanship of Scientific research at the University of Hail, Saudi Arabia under the project number SM2 for the year 1433 Hijri (2012-2013).

2. The authors would also like to thank Cornel University, USA for providing free access to their lung images database.

References

[1] M. Kass, A. Witkin, D. Terzopoulos, 1988. ―Snakes: Active contour models‖, International Journal of Computer Vision, vol. 1, no. 4, pp. 321–31.

[2] T. McInerney, D. Terzopoulos, 1996. ―Deformable models in medical image analysis: A survey‖, Medical Image Analysis, vol. 1, no. 2, pp. 91–108.

[3] J. A. Sethian., 1999. ―Level Set Methods and Fast Marching Methods‖, 2 ed. Cambridge, U.K.:

Cambridge Univ. Press.

[4] Y. Boykov, M. P. Jolly, 2000. ―Interactive organ segmentation using graph cuts‖, Proc. of Medical Imaging Computing and Computer-Assisted Interventions, MICCAI, Pittsburgh, Pennsylvania, pp. 276–86.

[5] Gleason, S.S., Paulus, M., Johnson, D., Sari-Sarraf, H., Abidi, M.A., 2000. ―Statistical-based deformable models with simultaneous optimization of object gray-level and shape characteristics‖, Proceedings 4^th IEEE Southwest Symp. Image Analysis and Interpretation, pp.

93–95.

[6] B. Ginneken, A. F. Frangi, J. J. Staal, B. M. Haar Romeny, 2002. ―Active Shape Model Segmentation with Optimal Features‖, IEEE Transactions on Medical Imaging; vol. 21, no. 8, pp. 924-33.

[7] Ingrid Sluimer, Mathias Prokop, Bram van Ginneken, 2005.―Toward automated segmentation of the pathological lung in CT‖, IEEE transactions on medical imaging, vol. 24, no. 8, pp.

1025-38.

[8] H. Lombaert, Y. Sun, L. Grady, C. Xu, 2005. ―A multilevel banded graph cuts method for fast image segmentation‖, Proceedings: Int. Conf. on Comp. Vision (ICCV), pp. 259–65.

[9] S. Chen, L. Cao, J. Liu, X. Tang, 2006. ―Automatic segmentation of lung fields from radiographic images of sars patients using a new graph cuts algorithm‖, Proceedings International Conference on Pattern Recognition ICPR, pp. 271–74.

[10] Farag AA, El-Baz A, Gimel‘farb A., 2006. ―Precise segmentation of multi-modal images‖, IEEE transactions on image processing, vol. 15, no. 4, pp. 952–68.

[11] Seghers, D., Loeckx, D., Maes, F., Vandermeulen, D., Suetens, 2007.―Minimal shape and intensity cost path segmentation‖, IEEE Transactions on Medical Imaging, vol. 26, no. 8, pp.

1115–29.

[12] Cao Lei, Li Xiaojian, Zhan Jie, Chen Wufan, 2008. ―Automated lung segmentation algorithm for CAD system of thoracic CT‖, Journal of Medical Colleges of PLA; vol. 23, no. 4, pp. 215–

22.

[13] Giorgio De Nunzio, Eleonora Tommasi, Antonella Agrusti, 2008. ―An innovative lung segmentation algorithm in ct images with accurate delimitation of the hiluspulmonis‖, IEEE Nuclear Science Symposium Conference Record, pp.5359-61.

[14] Jiantao Pu, Justus Roos, Chin A. Yi, Sandy Napel, Geoffrey D. Rubin, David S. Paik, 2008.

―Adaptive border marching algorithm: Automatic lung segmentation on chest CT images‖

Computerized Medical Imaging and Graphics, vol. no. 32: pp. 452–62.

[15] Lin-Yu Tseng, Li-Chin Huang, 2009. ―An Adaptive Thresholding Method for Automatic Lung Segmentation in CT Images‖, IEEE AFRICON proceedings, pp. 1-5.

[16] Nobuyuki Otsu, 1979. ―A Threshold Selection Method from Gray-Level Histograms‖, IEEE Transactions on Systems, Man and Cybernetics, vol. SMC-9.

[17] S. Rao Vantaram, Eli Saber, Sohail Dianat, Yang Hu, Vishwas Abhyankar, 2011. ―Semi-automatic 3-D segmentation of computed tomographic imagery by iterative gradient-driven volume growing‖, 18th IEEE International Conference on Image Processing, pp. 2857-60.

[18] R.Helen, N. Kamaraj, K. Selvi, 2011. ―Segmentation of Pulmonary Parenchyma in CT lung Images based on 2D Otsu optimized by PSO‖, IEEE Proceedings of ICETECT, pp. 536-41.

[19] A. Dawoud, 2011. ―Lung segmentation in chest radiographs by fusing shape information in iterative thresholding‖, Institute of Engineering & Technology (IET) Computer Vision, vol. 5, no. 3, 185–90.

[20] Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D, 2011. ―Global cancer statistics‖, CA A Cancer Journal for Clinicians, vol. 61, no. 2, pp. 69-90.

[21] S. Diciotti, 2008. 3-D Segmentation Algorithm of Small Lung Nodules in Spiral CT Images, IEEE Transactions on Information Technology in Biomedicine, vol 12, no. 1, pp.7-19.

[22] Trends in COPD (Chronic Bronchitis and Emphysema): Morbidity and Mortality, American Lung Association, Research and Program Services Division, August 2011.

http://www.europeanjournalofscientificresearch.com