Email this article 
Open Access
Subscription or Fee Access
Graph Cut Based Method for Automatic Lung Segmentation for Tuberculosis by using Screening Method in Chest Radiographs
Abstract
In medical imaging technique tuberculosis is an important challenging approach. Most of the peoples affected by the tuberculosis and tuberculosis are a very big disease after the HIV in India. The mortality rate of the peoples is high by affecting tuberculosis. Chest radiographs are also called as chest x ray or CXR. By using graph cut segmentation method is used to extract the lung region and texture and shape features are classified by using binary classifier. The postero anterior is used to automatically detect the tuberculosis. The existing smear microscopy is slow and unreliable. The ROC curve is used to illustrate the performance of the binary classifier. Three terms are classified as follows: Lung segmentation, feature computation, classification. Automated nodule detection is more nature applications of decision support/automation for CXR and CT.
Keywords
References
Dana Cobzas, Parisa Mosayebi, Albert Murtha, and Martin Jagersand,“Tumor invasion margin on the Riemannian space of brain fibers”,www.ncbi.nlm.nih.gov/pubmed/22154876, 2012
Olivier Clatz*, Maxime Sermesant, Pierre-Yves Bondiau, Hervé Delingette, Simon K. Warfield, Grégoire Malandain,and Nicholas Ayache,“Realistic Simulation of the 3-D Growth of BrainTumors in MR Images Coupling Diffusion With Biomechanical Deformation”, IEEE Transactions on Medical Imaging, vol. 24, No. 10, October 2005.
Ender Konukoglu, Olivier Clatz, Bjoern H. Menze, Bram Stieltjes, Marc-André Weber, Emmanuel Mandonnet,Hervé Delingette, and Nicholas Ayache,“Image Guided Personalization of Reaction-Diffusion Type Tumor Growth Models Using Modified Anisotropic Eikonal Equations”, IEEE Transactions on Medical Imaging, vol. 29, No. 1, January 2010
R Rockne, J K Rockhill, M Mrugala, A M Spence, I Kalet,K Hendrickson, A Lai, T Cloughesy, E C Alvord Jr and K R Swanson,“Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach”, Phys. Med. Biol. 55 (2010) 3271–3285.
Cosmina Hogea, Christos Davatzikos, George Biros,“An image-driven parameter estimation problem for are action-diffusion glioma growth model with mass effects”, Biomedical Image Analysis, 2007
Xinjian Chen, Ronald M. Summers, and Jianhua Yao,“Kidney Tumor Growth Prediction by Coupling Reaction–Diffusion and Biomechanical Model”,IEEE Transactions on Biomedical Engineering, vol. 60, No. 1, January 2013
Thierry Colin, Angelo Iollo, Damiano Lombardi and Olivier Saut“System Identification In Tumor Growth Modeling Using Semi-Empirical Eigen functions”, Mathematical Models and Methods in Applied Sciencesvol. 22, No. 6 (2012)
Rui Shen, , Irene Cheng and Anup Basu, “A Hybrid Knowledge-Guided Detection Technique for Screening of Infectious Pulmonary Tuberculosis From Chest Radiographs”, IEEE Transactions on Biomedical Engineering, vol. 57, No. 11, November 2010.
Dieter Seghers, Dirk Loeckx, Frederik Maes, Dirk Vandermeulen, and Paul Suetens, “Minimal Shape and Intensity Cost Path Segmentation”, IEEE Transactions on Medical Imaging, vol. 26, No. 8, August 2007
Guoying Zhao and Matti Pietikainen, “Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions”, IEEE Transactions On Pattern Analysis And Machine Intelligence, vol. 29, No. 6, June 2007
R. Shen, I. Cheng, and A. Basu, “A hybrid knowledge-guided detection technique for screening of infectious pulmonary tuberculosis from chest radiographs,” IEEE Trans. Biomed. Eng., vol. 57, No. 11, pp. 2646–2656, Nov. 2010
S. Jaeger, C. Casas-Delucchi, M. Cardoso, and K. Palaniappan, “Classification of cell cycle phases in 3D confocal microscopy using PCNA and chromo center features,” in Proc. Indian Conf. Comput. Vis., Graph., Image Process., 2010, pp. 412–418
C. Pangilinan, A. Divekar, G. Coetzee, D. Clark, B. Fourie, F. Lure, and S. Kennedy, “Application of stepwise binary decision classification for reduction of false positives in tuberculosis detection from smeared slides,” presented at the Int. Conf. Image. Signal Process. Healthcare Technol., Washington, DC, 2011 screening of chest radiographs,” in SPIE Newsroom, 2011.
T. Xu, I. Cheng, and M. Mandal, “Automated cavity detection of infectious pulmonary tuberculosis in chest radiographs,” in Proc. Int. IEEE Eng. Med. Biol. Soc., 2011, pp. 5178–5181.
L. Hogeweg, C. I. Sánchez, P. A. de Jong, P. Maduskar, and B. van Ginneken, “Clavicle segmentation in chest radiographs,” Med. Image Anal., vol. 16, no. 8, pp. 1490–1502, 2012.
M. Freedman, S. Lo, J. Seibel, and C. Bromley, “Lung nodules: Improved detection with software that suppresses the rib and clavicle on chest radiographs,” Radiology, vol. 260, no. 1, pp. 265–273, 2011.
Y. Arzhaeva, D. Tax, and B. Van Ginneken, “Dissimilarity-based classification in the absence of local ground truth: Application to the diagnostic interpretation of chest radiographs,” Pattern Recognit., vol. 42, no. 9, pp. 1768–1776, 2009.
S. Jaeger, A. Karargyris, S. Candemir, J. Siegelman, L. Folio, S. Antani, and G. Thoma, “Automatic screening for tuberculosis in chest radiographs: A survey,” Quant. Image. Med. Surg., vol. 3, no. 2, pp. 89–99, 2013.
L. R. Folio, Chest Imaging: An Algorithmic Approach to Learning. New York: Springer, 2012. S. Jaeger, S. Antani, and G. Thoma, “Tuberculosis.
S. Jaeger, S. Antani, and G. Thoma, “Tuberculosis screening of chest radiographs,” in SPIE Newsroom, 2011.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.