Lung Disease Identification and Segmentation in Medical Images

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2019 by IJETT Journal
Volume-67 Issue-8
Year of Publication : 2019
Authors : M.Mary Adline Priya, Dr.S.Joseph Jawhar.
DOI :  10.14445/22315381/IJETT-V67I8P215


MLA Style: M.Mary Adline Priya, Dr.S.Joseph Jawhar  "Lung Disease Identification and Segmentation in Medical Images" International Journal of Engineering Trends and Technology 67.8 (2019):87-91.

APA Style:M.Mary Adline Priya, Dr.S.Joseph Jawhar. Lung Disease Identification and Segmentation in Medical Images  International Journal of Engineering Trends and Technology, 67(8),87-91.

The classification and identification of the disease in medical images were helpful in biomedical applications. The process of segmentation of the diseased portion in the lung lobe images were done based on Toboggan algorithm. The lung lobes were segmented from the input images based on gradient estimation following original Toboggan algorithm. If the segmented lung lobes were disease affected means then the identification of disease location is done. The classification process is employed using SVM classifier with the help of features extracted from lung lobes using texture identification. From the gradient estimated lung lesion inside the segmented lung lobes were extracted based on the improved Toboggan algorithm. Contours were extracted over the identified lung lesion regions. The overall performance of the process were measured based on the performance metrics.


[1] D.B. Hassen and H.Taleb, “Automatic detection of lesions in lung regions that are segmented using spatial relations”, Clinical Imaging, vol.37.pp. 498-503, 2013.
[2] L.Wang, H.Lin, X.Huang, B.Wang, Y.Chen, “A 3D segmentation and visualization scheme for solid and nonsolid lung lesions based on Gaussian filtering regularized level set”, International conference on 3D vision, pp.67- 73,2014.
[3] A M Santos, A O de C Filho, A C Silva, A C de Paiva, R A Nunes, M Gattass, “Automatic detection of small lung nodules in 3D CT data using Gaussian mixture models, Tsallis entrophy and SVM”, Engg. Apllications of AI, vol. 36, pp. 27-39, 2014.
[4] F.Rossi, A.A.Abd.Rahni, “Combination of low level processing and active contour techniques for semiautomated volumetric lung lesion segmentation from thoracic CT images”, IEEE Trans. On Biomedical engineering and sciences, vol. 15, pp. 26-30, 2015.
[5] Lim J. Seelan L. Padma Suresh, S H K Veni, “Automatic extraction of lung lesion by using optimized toboggan based approach with feature normalization and transfer learning methods”, IEEE Trans. on emerging tech. trends, vol. 16, pp. 18-27, 2016.
[6] Yosefina Finsensia Riti, Hanung Adi Nugroho, Sunu Wibirama, Budi Windarta,Lina Choridah, “Feature extraction for lesion margin characteristic classification from CT scan lungs Image”, IEEE Trans. on 2016 1st International Conference on Information Technology, Information Systems and Electrical Engineering, vol. 16, pp. 54-58, 2016.
[7] D. M. Campos, A. Simões, I. Ramos, A. Campilho, “Feature-Based Supervised Lung Nodule Segmentation”, pp. 23-26, 2014.
[8] S. Diciotti, S. Lombardo, M. Falchini, G. Picozzi, M. Mascalchi, “Automated segmentation refinement of small lung nodules in CT scans by local shape analysis”, IEEE Trans. Biomed. Eng., vol. 58, no. 12, pp. 3418-3428, 2011.
[9] S. Candemir, S. Jaeger, K. Palaniappan, J. P. Musco, R. K. Singh, Z. Xue, A. Karargyris, S. Antani, G. Thoma, C. J. McDonald, “Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration”, IEEE Trans. Med. Imaging, vol. 33, no. 2, pp. 577-590, 2014.
[10] A. a Farag, H. E. A. El Munim, J. H. Graham, A. a Farag, “A novel approach for lung nodules segmentation in chest CT using level sets.”, IEEE Trans. Image Process., vol. 22, no. 12, pp. 5202-5213, 2013.
[11] Vigneshwar. R, Karthic.K, Karthick.A and Senthamizhselvi.R “Lung Lesion Extraction Using Improved Toboggan Based Algorithm” ,International Journal of Advance Research, Ideas and Innovations in Technology,Volume3, Issue2, pg. no.774-779, 2017.
[12] S. Sun, Y. Guo, Y. Guan, H. Ren, “Juxta-Vascular Nodule Segmentation Based on the Flowing Entropy and Geodesic Distance Feature”, Scientia Sinica(Informationis), vol. 61, pp. 1136-1146, 2013.
[13] Y. Gu, V. Kumar, L. O. Hall, D. B. Goldgof, C. Y. Li, R. Korn, C. Bendtsen, E. R. Velazquez, A. Dekker, H. Aerts, P. Lambin, X. Li, J. Tian, R. A. Gatenby, R. J. Gillies, “Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach”, Pattern Recognition, vol. 46, no. 3, pp. 692-702, 2013.
[14] C. Bendtsen, M. Kietzmann, R. Korn, P. D. Mozley, G. Schmidt, G. Binnig, “X-Ray computed tomography: Semi automated volumetric analysis of late-stage lung tumors as a basis for response assessments”, Int. J. Biomed. Imaging, 2011.
[15] T. Kubota, A. K. Jerebko, M. Dewan, M. Salganicoff, A. Krishnan, “Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models”, Med. Image Anal., vol. 15, no. 1, pp. 133-154, 2011.
[16] A. Mansoor, U. Bagci, Z. Xu, B. Foster, K. N. Olivier, J. M. Elinoff, “A generic approach to pathological lung segmentation”, IEEE Trans Med Imaging, vol. 33, pp. 2293-2310, Dec. 2014.
[17] R. Siegel, D. Naishadham, A. Jemal, “Cancer statistics 2017”, CA Cancer J Clin, vol. 63, pp. 11-30, Jan. 2017.
[18] J. Song and C. Yang et al, “A New Quantitative Radiomics Approach for Non- Small Cell Lung Cancer (NSCLC) Prognosis”, The 101ndInt. Conf. Radiological Society of North America, Chicago, Illinois, November 29– December 2015.
[19] H. J. W. L. Aerts, E. R. Velazquez, R. T. H. Leijenaar, C. Parmar, P.Grossmann, S. Cavalho, J. Bussink, R. Monshouwer, B. Haibe-Kains,D. Rietveld, F. Hoebers, M. M. Rietbergen, C. R. Leemans, A. Dekker,J. Quackenbush, R. J. Gillies, and P. Lambin, “Decoding tumourPhenotype by noninvasive imaging using a quantitative radiomics Approach.” Nat. Communes., vol. 5, pp. 4006, 2014.
[20] Bian and Zijian et al., “Accurate airway centerline extraction basedOn topological thinning using graph theoretic analysis”, Bio-medical Materials and engineering, vol. 24, no. 6, pp. 3239–3249, 2014.
[21] Caiyum Yang,Li Fan , Kun Wang ,Feng Yang,shiyuan Liu, and Jie Tian, “Lung lesion extraction using a toboggan based growing automatic segmentation approach”, IEEE Transactions on medical imaging, vol.35 no.1, January 2016.
[22] D. M. Campos, A. Simões, I. Ramos, and A. Campilho, “Feature-Based Supervised Lung Nodule Segmentation”, pp. 23–26, 2014.
[23] S. Candemir, S. Jaeger, K. Palaniappan, J. P. Musco, R. K. Singh, “Lung segmentation in chest radiographs using anatomical atlases with Non rigid registration”, IEEE Trans. Med. Imaging, vol. 33, no. 2, pp. 577–590, 2014.
[24] B. Lassen, E. M. Van Rikxoort, M. Schmidt, S. Kerkstra, B. Van Ginneken and J. M. Kuhnigk, “Automatic segmentation of the pulmonary Lobes from chest CT scans based on Fissures, Vessels, Bronchi”, IEEE Trans. Med. Imaging, vol. 32, no. 2, pp. 210–222, 2013.
[25] M. Nakata, H. Saeki, I. Takata, Y. Segawa, H. Mogami, K. Mandai, andK. Eguchi, “Focal ground-glass opacity detected by low-dose helical CT”, Chest, vol. 121, no. 5, pp. 1464–1467, 2002.
[26] W. H. Organization, “Description of the global burden of NCDs, their Risk factors and determinants,” Geneva, Switzerland: World Health Organization, 2011.

Lung cancer, SVM, Lung lesion, Computed Tomography.