Automated Approach for Detecting Tuberculosis using Chest Radiographs

Authors

  • Dr. Sreeja Mole S S  Department of ECE, Narayana Guru College of Engineering, Manjalumoodu, Kanyakumari, Tamil Nadu, India
  • Aiswarya A K  Department of ECE, Narayana Guru College of Engineering, Manjalumoodu, Kanyakumari, Tamil Nadu, India
  • Akhila L S  Department of ECE, Narayana Guru College of Engineering, Manjalumoodu, Kanyakumari, Tamil Nadu, India
  • Akhila S  Department of ECE, Narayana Guru College of Engineering, Manjalumoodu, Kanyakumari, Tamil Nadu, India

Keywords:

Computer-aided detection and diagnosis, lung, pattern recognition and classification, segmentation, tuberculosis (TB), X-ray imaging.

Abstract

Tuberculosis is one of the major health problem in many parts of the world. Due to multi-drug-resistant bacterial strains have increased the problem, tuberculosis still remains a challenge. Mortality rates of patients with tuberculosis are high when left undiagnosed and untrearted. Standard diagnostics depends on methods developed in the last century which are slow and unreliable. In an effort to reduce the complexity of the disease, this paper presents our automated approach for detecting tuberculosis using chest radiographs. At first we extract the lung region using a graph cut segmentation method. From this extracted lung region, we compute a set of texture and shape features, which enable the X-rays to be classified as normal or abnormal using a binary classifier.

References

  1. World Health Org., Global tuberculosis report 2012
  2. World Health Org., Global tuberculosis control 2011 2011.
  3. Stop TB Partnership, World Health Org. , The Global Plan to Stop TB 2011-2015 2011. 244 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 33, NO. 2, FEBRUARY 2014
  4. S. Candemir, S. Jaeger, K. Palaniappan, S. Antani, and G. Thoma, "Graph-cut based automatic lung boundary detection in chest radiographs," in Proc. IEEE Healthcare Technol. Conf.: Translat. Eng. Health Med., 2012, pp. 31-34.
  5. S. Candemir, K. Palaniappan, and Y. Akgul, "Multi-class regularization parameter learning for graph cut image segmentation," in Proc. Int. Symp. Biomed. Imag., 2013, pp. 1473-1476.
  6. S. Jaeger, A. Karargyris, S. Antani, and G. Thoma, "Detecting tuberculosis in radiographs using combined lung masks," in Proc. Int. Conf. IEEE Eng. Med. Biol. Soc., 2012, pp. 4978-4981.
  7. C. Leung, "Reexamining the role of radiography in tuberculosis case finding," Int. J. Tuberculosis Lung Disease, vol. 15, no. 10, pp. 1279-1279, 2011.
  8. L. R. Folio, Chest Imaging: An Algorithmic Approach to Learning. New York: Springer, 2012.
  9. S. Jaeger, S. Antani, and G. Thoma, "Tuberculosis screening of chest radiographs," in SPIE Newsroom, 2011.
  10. C. Daley, M. Gotway, and R. Jasmer, "Radiographic manifestations of tuberculosis," in A Primer for Clinicians. San Francisco, CA: Curry International Tuberculosis Center, 2009.
  11. J. Burrill, C. Williams, G. Bain, G. Conder, A. Hine, and R. Misra, "Tuberculosis: A radiologic review," Radiographics, vol. 27, no. 5, pp. 1255-1273, 2007.
  12. R. Gie, Diagnostic Atlas of Intrathoracic Tuberculosis in Children. : International Union Against Tuberculosis and Lung Disease (IUATLD), 2003.
  13. A. Leung, "Pulmonary tuberculosis: The essentials," Radiology, vol. 210, no. 2, pp. 307-322, 1999.
  14. B. van Ginneken, L. Hogeweg, andM. Prokop, "Computer-aided diagnosis in chest radiography: Beyond nodules," Eur. J. Radiol., vol. 72, no. 2, pp. 226-230, 2009.
  15. G. Lodwick, "Computer-aided diagnosis in radiology: A research plan," Invest. Radiol., vol. 1, no. 1, p. 72, 1966.
  16. G. Lodwick, T. Keats, and J. Dorst, "The coding of Roentgen images for computer analysis as applied to lung cancer," Radiology, vol. 81, no. 2, p. 185, 1963.
  17. S. Sakai, H. Soeda, N. Takahashi, T. Okafuji, T. Yoshitake, H. Yabuuchi, I. Yoshino, K. Yamamoto, H. Honda, and K. Doi, "Computeraided nodule detection on digital chest radiography: Validation test on consecutive T1 cases of resectable lung cancer," J. Digit. Imag., vol. 19, no. 4, pp. 376-382, 2006.
  18. J. Shiraishi, H. Abe, F. Li, R. Engelmann, H. MacMahon, and K. Doi, "Computer-aided diagnosis for the detection and classification of lung cancers on chest radiographs: ROC analysis of radiologists’ performance," Acad. Radiol., vol. 13, no. 8, pp. 995-1003, 2006.
  19. S. Kakeda, J. Moriya, H. Sato, T. Aoki, H. Watanabe, H. Nakata, N. Oda, S. Katsuragawa, K. Yamamoto, and K. Doi, "Improved detection of lung nodules on chest radiographs using a commercial computer- aided diagnosis system," Am. J. Roentgenol., vol. 182, no. 2, pp. 505-510, 2004.
  20. K. Doi, "Current status and future potential of computer-aided diagnosis inmedical imaging,"Br. J. Radiol., vol. 78, no. 1, pp. 3-19, 2005.
  21. B. Van Ginneken, B. ter Haar Romeny, and M. Viergever, "Computeraided diagnosis in chest radiography: A survey," IEEE Trans. Med. Imag., vol. 20, no. 12, pp. 1228-1241, Dec. 2001.
  22. B. Van Ginneken, M. Stegmann, and M. Loog, "Segmentation of anatomical structures in chest radiographs using supervised methods: A comparative study on a public database," Med. Image Anal., vol. 10, no. 1, pp. 19-40, 2006.
  23. B. van Ginneken and B. ter Haar Romeny, "Automatic segmentation of lung fields in chest radiographs," Med. Phys., vol. 27, no. 10, pp. 2445-2455, 2000.
  24. A. Dawoud, "Fusing shape information in lung segmentation in chest radiographs," Image Anal. Recognit., pp. 70-78, 2010.
  25. B. van Ginneken, S. Katsuragawa, B. ter Haar Romeny, K. Doi, andM. Viergever, "Automatic detection of abnormalities in chest radiographs using local texture analysis," IEEE Trans. Med. Imag., vol. 21, no. 2, pp. 139-149, Feb. 2002.
  26. L. Hogeweg, C. Mol, P. de Jong, R. Dawson, H. Ayles, and B. van Ginneken, "Fusion of local and global detection systems to detect tuberculosis in chest radiographs," in Proc. MICCAI, 2010, pp. 650-657.
  27. 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.
  28. T. Xu, I. Cheng, andM.Mandal, "Automated cavity detection of infectious pulmonary tuberculosis in chest radiographs," in Proc. Int. IEEE Eng. Med. Biol. Soc., 2011, pp. 5178-5181.
  29. 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.
  30. 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.
  31. 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.
  32. 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. Imag. Med. Surg., vol. 3, no. 2, pp. 89-99, 2013.
  33. 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. Imag. Signal Process. Healthcare Technol., Washington, DC, 2011.
  34. C. Boehme, P. Nabeta, D. Hillemann, M. Nicol, S. Shenai, F. Krapp, J. Allen, R. Tahirli, R. Blakemore, and R. Rustomjee et al., "Rapid molecular detection of tuberculosis and rifampin resistance," New Eng. J. Med., vol. 363, no. 11, pp. 1005-1015, 2010.
  35. J. Shiraishi, S. Katsuragawa, J. Ikezoe, T. Matsumoto, T.Kobayashi, K. Komatsu, M. Matsui, H. Fujita, Y. Kodera, and K. Doi, "Development of a digital image database for chest radiographs with and without a lung nodule," Am. J. Roentgenol., vol. 174, no. 1, pp. 71-74, 2000.
  36. Y. Boykov and G. Funka-Lea, "Graph cuts and efficient n-d image segmentation," Int. J. Comput. Vis., vol. 70, pp. 109-131, 2006.
  37. Y. Boykov, O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 11, pp. 1222-1239, Nov. 2001.
  38. S. Jaeger, C. Casas-Delucchi, M. Cardoso, and K. Palaniappan, "Dual channel colocalization for cell cycle analysis using 3D confocal microscopy," in Proc. Int. Conf. Pattern Recognit., 2010, pp. 2580-2583.
  39. S. Jaeger, C. Casas-Delucchi, M. Cardoso, and K. Palaniappan, "Classification of cell cycle phases in 3D confocal microscopy using PCNA and chromocenter features," in Proc. Indian Conf. Comput. Vis., Graph., Image Process., 2010, pp. 412-418.
  40. K. Palaniappan, F. Bunyak, P. Kumar, I. Ersoy, S. Jaeger, K. Ganguli, A. Haridas, J. Fraser, R. Rao, and G. Seetharaman, "Efficient feature extraction and likelihood fusion for vehicle tracking in low frame rate airborne video," in Proc. Int. Conf. Inf. Fusion, 2010, pp. 1-8.
  41. M. Linguraru, S. Wang, F. Shah, R. Gautam, J. Peterson, W. Linehan, and R. Summers, "Computer-aided renal cancer quantification and classification from contrast-enhanced CT via histograms of curvature- related features," in Proc. Int. Conf. IEEE Eng. Med. Biol. Soc., 2009, pp. 6679-6682.
  42. R. Pelapur, S. Candemir, F. Bunyak,M. Poostchi, G. Seetharaman, and K. Palaniappan, "Persistent target tracking using likelihood fusion in wide-area and full motion video sequences," in Proc. Int. Conf. Inf. Fusion, 2012, pp. 2420-2427.
  43. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proc. Int. Conf. Comp. Vis. Patt. Recognit., 2005, vol. 1, pp. 886-893.
  44. L. Chen, R. Feris, Y. Zhai, L. Brown, and A. Hampapur, "An integrated system for moving object classification in surveillance videos," in Proc. Int. Conf. Adv. Video Signal Based Surveill., 2008, pp. 52-59.
  45. F. Han, Y. Shan, R. Cekander, H. Sawhney, and R. Kumar, "A two-stage approach to people and vehicle detection with HOG-based SVM," in Performance Metrics Intell. Syst. Workshop, Gaithersburg, MD, 2006, pp. 133-140.
  46. X.Wang, T. X. Han, and S. Yan, "An HOG-LBP human detector with partial occlusion handling," in Proc. Int. Conf. Comput. Vis., 2009, pp. 32-39.
  47. T. Ojala, M. Pietikäinen, and T. Mäenpää, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971-987, Jul. 2002.
  48. T. Ojala, M. Pietikäinen, and D. Harwood, "A comparative study of texture measures with classification based on feature distributions," Pattern Recognit., vol. 29, pp. 51-59, 1996. JAEGER et al.: AUTOMATIC TUBERCULOSIS SCREENING USING CHEST RADIOGRAPHS 245
  49. G. Zhao and M. Pietikainen, "Dynamic texture recognition using local binary patterns with an application to facial expressions," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 6, pp. 915-928, Jun. 2007.
  50. P. Dollar, C. Wojek, B. Schiele, and P. Perona, "Pedestrian detection: An evaluation of the state of the art," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 4, pp. 743-761, Apr. 2012.
  51. A. Hafiane, G. Seetharaman, K. Palaniappan, and B. Zavidovique, "Rotationally invariant hashing of median binary patterns for texture classification," in Proc. Int. Conf. Image Anal. Recognit., 2008, pp. 619-629.
  52. A. Frangi, W. Niessen, K. Vincken, and M. Viergever, "Multiscale vessel enhancement filtering," in Proc. MICCAI, 1998, pp. 130-137.
  53. F. Bunyak, K. Palaniappan,O. Glinskii, V.Glinskii, V. Glinsky, and V. Huxley, "Epifluorescence-based quantitative microvasculature remodeling using geodesic level-sets and shape-based evolution," in Proc. Int. Conf. IEEE Eng. Med. Biol. Soc., 2008, pp. 3134-3137.
  54. M. Simpson, D. You, M. Rahman, D. Demner-Fushman, S. Antani, and G. Thoma, "ITI’s participation in the ImageCLEF 2012 medical retrieval and classification tasks," in CLEF 2012 Working Notes, 2012.
  55. C.-R. Shyu, M. Klaric, G. Scott, A. Barb, C. Davis, and K. Palaniappan, "GeoIRIS: Geospatial information retrieval and indexing system—Content mining, semantics modeling, complex queries," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 4, pp. 839-852, Apr. 2007

International Journal of Scientific Research in Science, Engineering and Technology

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2015-06-15

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[1]
Dr. Sreeja Mole S S, Aiswarya A K, Akhila L S, Akhila S, " Automated Approach for Detecting Tuberculosis using Chest Radiographs , International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 1, Issue 3, pp.125-129, May-June-2015.