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A Survey of CAD Methods for Tuberculosis Detection in Chest Radiographs

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Soft Computing: Theories and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 742))

Abstract

Tuberculosis is a highly infectious disease and the second largest killer worldwide. Every year, millions of new cases and deaths are reported due to tuberculosis. In the developing countries, tuberculosis suspect cases are enormous, and hence, a large number of radiologists are required to perform the mass screening. Therefore attempts have been made to design computer-aided diagnosis (CAD) systems for automatic mass screening. A CAD system generally consists of four phases, namely, preprocessing, segmentation, feature extraction, and classification. In this paper, we present a survey of the recent approaches used in different phases of chest radiographic CAD system for tuberculosis detection.

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References

  1. Duncan, J.S., Ayache, N.: Medical image analysis: progress over two decades and the challenges ahead. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 85–106 (2000)

    Article  Google Scholar 

  2. Van Ginneken, B., Romeny, B.T.H., Viergever, M.A.: Computer-aided diagnosis in chest radiography: a survey. IEEE Trans. Med. Imaging 20(12), 1228–1241 (2001)

    Article  Google Scholar 

  3. Mehta, I.C., Ray, A.K., Khan, Z.J.: Lung cancer detection using computer aided diagnosis in chest radiograph: A survey and analysis. IETE Tech. Rev. 22(5), 385–393 (2005)

    Article  Google Scholar 

  4. Sluimer, I., Schilham, A., Prokop, M., van Ginneken, B.: Computer analysis of computed tomography scans of the lung: a survey. IEEE Trans. Med. Imaging 25(4), 385–405 (2006)

    Article  Google Scholar 

  5. Doi, K.: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 31(4), 198–211 (2007)

    Article  Google Scholar 

  6. Li, B., Tian, L., Ou, S.: CAD for identifying malignant lung nodules in early diagnosis: a survey. Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 26(5), 1141–1145 (2009)

    Google Scholar 

  7. Van Ginneken, B., Stegmann, M.B., Loog, M.: Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. Med. Image Anal. 10(1), 19–40 (2006)

    Article  Google Scholar 

  8. Jaeger, S., Karargyris, A., Candemir, S., Siegelman, J., Folio, L., Antani, S., Thoma, G.: Automatic screening for tuberculosis in chest radiographs: a survey. Quant. Imaging Med. Surg. 3(2), 89 (2013)

    Google Scholar 

  9. Sezn, M.I., Teklap, A.M., Schaetzing, R.: Automatic anatomically selective image enhancement in digital chest radiography. IEEE Trans. Med. Imaging 8(2), 154–162 (1989)

    Article  Google Scholar 

  10. Tahoces, P.G., Correa, J., Souto, M., Gonzalez, C., Gomez, L., Vidal, J.J.: Enhancement of chest and breast radiographs by automatic spatial filtering. IEEE Trans. Med. Imaging 10(3), 330–335 (1991)

    Article  Google Scholar 

  11. Chang, D.C., Wu, W.R.: Image contrast enhancement based on a histogram transformation of local standard deviation. IEEE Trans. Med. Imaging 17(4), 518–531 (1998)

    Article  MathSciNet  Google Scholar 

  12. Hogeweg, L., Sanchez, C.I., van Ginneken, B.: Suppression of translucent elongated structures: applications in chest radiography. IEEE Trans. Med. Imaging 32(11), 2099–2113 (2013)

    Article  Google Scholar 

  13. Hogeweg, L., Sánchez, C.I., de Jong, P.A., Maduskar, P., van Ginneken, B.: Clavicle segmentation in chest radiographs. Med. Image Anal. 16(8), 1490–1502 (2012)

    Article  Google Scholar 

  14. Simkó, G., Orbán, G., Máday, P., Horváth, G.: Elimination of clavicle shadows to help automatic lung nodule detection on chest radiographs. In: 4th European Conference of the International Federation for Medical and Biological Engineering, pp. 488–491. Springer, Berlin, Heidelberg (2009)

    Google Scholar 

  15. Schalekamp, S., van Ginneken, B., Meiss, L., Peters-Bax, L., Quekel, L.G., Snoeren, M.M.,… Schaefer-Prokop, C.M.: Bone suppressed images improve radiologists’ detection performance for pulmonary nodules in chest radiographs. Eur. J. Radiol. 82(12), 2399–2405 (2013)

    Google Scholar 

  16. Freedman, M.T., Lo, S.C.B., Seibel, J.C., Bromley, C.M.: Lung nodules: improved detection with software that suppresses the rib and clavicle on chest radiographs. Radiology 260(1), 265–273 (2011)

    Article  Google Scholar 

  17. Li, F., Hara, T., Shiraishi, J., Engelmann, R., MacMahon, H., Doi, K.: Improved detection of subtle lung nodules by use of chest radiographs with bone suppression imaging: receiver operating characteristic analysis with and without localization. Am. J. Roentgenol. 196(5), W535–W541 (2011)

    Article  Google Scholar 

  18. Hogeweg, L., Sánchez, C.I., Melendez, J., Maduskar, P., Story, A., Hayward, A., van Ginneken, B.: Foreign object detection and removal to improve automated analysis of chest radiographs. Med. Phys. 40(7) (2013)

    Google Scholar 

  19. Cheng, D., Goldberg, M.: An algorithm for segmenting chest radiographs. In: Proceedings of the SPIE, vol. 1001, pp. 261–268, Oct, 1988

    Google Scholar 

  20. Li, L., Zheng, Y., Kallergi, M., Clark, R.A.: Improved method for automatic identification of lung regions on chest radiographs. Acad. Radiol. 8(7), 629–638 (2001)

    Article  Google Scholar 

  21. Duryea, J., Boone, J.M.: A fully automated algorithm for the segmentation of lung fields on digital chest radiographic images. Med. Phys. 22(2), 183–191 (1995)

    Article  Google Scholar 

  22. Pietka, E.: Lung segmentation in digital radiographs. J. Digit. Imaging 7(2), 79–84 (1994)

    Article  Google Scholar 

  23. Armato, S.G., Giger, M.L., Ashizawa, K., MacMahon, H.: Automated lung segmentation in digital lateral chest radiographs. Med. Phys. 25(8), 1507–1520 (1998)

    Article  Google Scholar 

  24. Ahmad, W.S.H.M.W., Zaki, W.M.D.W., Fauzi, M.F.A.: Lung segmentation on standard and mobile chest radiographs using oriented Gaussian derivatives filter. Biomed. Eng. Online 14(1), 20 (2015)

    Article  Google Scholar 

  25. McNitt-Gray, M.F., Huang, H.K., Sayre, J.W.: Feature selection in the pattern classification problem of digital chest radiograph segmentation. IEEE Trans. Med. Imaging 14(3), 537–547 (1995)

    Article  Google Scholar 

  26. Tsujii, O., Freedman, M.T., Mun, S.K.: Automated segmentation of anatomic regions in chest radiographs using an adaptive-sized hybrid neural network. Med. Phys. 25(6), 998–1007 (1998)

    Article  Google Scholar 

  27. Van Ginneken, B., ter Haar Romeny, B.M.: Automatic segmentation of lung fields in chest radiographs. Med. Phys. 27(10), 2445–2455 (2000)

    Article  Google Scholar 

  28. Shi, Z., Zhou, P., He, L., Nakamura, T., Yao, Q., Itoh, H.: Lung segmentation in chest radiographs by means of Gaussian kernel-based FCM with spatial constraints. In: Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009. FSKD’09, vol. 3, pp. 428–432, Aug, 2009. IEEE

    Google Scholar 

  29. Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models-their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995)

    Article  Google Scholar 

  30. Van Ginneken, B., Frangi, A.F., Staal, J.J., ter Haar Romeny, B.M., Viergever, M.A.: Active shape model segmentation with optimal features. IEEE Trans. Med. Imaging 21(8), 924–933 (2002)

    Article  Google Scholar 

  31. Xu, T., Mandal, M., Long, R., Basu, A.: Gradient vector flow based active shape model for lung field segmentation in chest radiographs. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009. EMBC 2009, pp. 3561–3564, Sept, 2009. IEEE

    Google Scholar 

  32. Wu, G., Zhang, X., Luo, S., Hu, Q.: Lung segmentation based on customized active shape model from digital radiography chest images. J. Med. Imaging Health Inform. 5(2), 184–191 (2015)

    Article  Google Scholar 

  33. Lee, J.S., Wu, H.H., Yuan, M.Z.: Lung segmentation for chest radiograph by using adaptive active shape models. Biomed. Eng. Appl. Basis Commun. 22(02), 149–156 (2010)

    Article  Google Scholar 

  34. Xu, T., Mandal, M., Long, R., Cheng, I., Basu, A.: An edge-region force guided active shape approach for automatic lung field detection in chest radiographs. Comput. Med. Imaging Graph. 36(6), 452–463 (2012)

    Article  Google Scholar 

  35. Dawoud, A.: Lung segmentation in chest radiographs by fusing shape information in iterative thresholding. IET Comput. Vision 5(3), 185–190 (2011)

    Article  Google Scholar 

  36. Shao, Y., Gao, Y., Guo, Y., Shi, Y., Yang, X., Shen, D.: Hierarchical lung field segmentation with joint shape and appearance sparse learning. IEEE Trans. Med. Imaging 33(9), 1761–1780 (2014)

    Article  Google Scholar 

  37. Zhang, G.D., Guo, Y.F., Gao, S., Guo, W.: Lung segmentation in feature images with gray and shape information. In: Applied Mechanics and Materials, vol. 513, pp. 3069–3072. Trans Tech Publications (2014)

    Google Scholar 

  38. Annangi, P., Thiruvenkadam, S., Raja, A., Xu, H., Sun, X., Mao, L.: A region based active contour method for x-ray lung segmentation using prior shape and low level features. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 892–895, Apr, 2010. IEEE

    Google Scholar 

  39. Lee, W.L., Chang, K., Hsieh, K.S.: Unsupervised segmentation of lung fields in chest radiographs using multiresolution fractal feature vector and deformable models. Med. Biol. Eng. Comput. 54(9), 1409–1422 (2016)

    Article  Google Scholar 

  40. Candemir, S., Jaeger, S., Palaniappan, K., Antani, S., Thoma, G.: Graph-cut based automatic lung boundary detection in chest radiographs. In: IEEE Healthcare Technology Conference: Translational Engineering in Health & Medicine, pp. 31–34, Nov, 2012

    Google Scholar 

  41. Candemir, S., Jaeger, S., Palaniappan, K., Musco, J.P., Singh, R.K., Xue, Z., … McDonald, C.J.: Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans. Med. Imaging 33(2), 577–590 (2014)

    Google Scholar 

  42. Sarkar, S., Chaudhuri, S.: Automated detection of infiltration and cavitation in digital chest radiographs of chronic pulmonary tuberculosis. In: Proceedings of the 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine, vol. 3, pp. 1185–1186. IEEE (1997)

    Google Scholar 

  43. Koeslag, A., de Jager, G.: Computer aided diagnosis of miliary tuberculosis. In: Proceedings of the Pattern Recognition Association of South Africa (2001)

    Google Scholar 

  44. Leibstein, J.M., Nel, A.L.: Detecting tuberculosis in chest radiographs using image processing techniques. University of Johannesburg (2006)

    Google Scholar 

  45. Song, Y.L., Yang, Y.: Localization algorithm and implementation for focal of pulmonary tuberculosis chest image. In: 2010 International Conference on Machine Vision and Human-Machine Interface (MVHI), pp. 361–364, Apr, 2010. IEEE

    Google Scholar 

  46. Tan, J.H., Acharya, U.R., Tan, C., Abraham, K.T., Lim, C.M.: Computer-assisted diagnosis of tuberculosis: a first order statistical approach to chest radiograph. J. Med. Syst. 36(5), 2751–2759 (2012)

    Article  Google Scholar 

  47. Xu, T., Cheng, I., Long, R., Mandal, M.: Novel coarse-to-fine dual scale technique for tuberculosis cavity detection in chest radiographs. EURASIP J. Image Video Process. 2013(1), 3 (2013)

    Article  Google Scholar 

  48. Van Ginneken, B., Katsuragawa, S., ter Haar Romeny, B.M., Doi, K., Viergever, M.A.: Automatic detection of abnormalities in chest radiographs using local texture analysis. IEEE Trans. Med. Imaging 21(2), 139–149 (2002)

    Article  Google Scholar 

  49. Hogeweg, L., Sánchez, C.I., Maduskar, P., Philipsen, R., Story, A., Dawson, R.: van Ginneken, B.: Automatic detection of tuberculosis in chest radiographs using a combination of textural, focal, and shape abnormality analysis. IEEE Trans. Med. Imaging 34(12), 2429–2442 (2015)

    Article  Google Scholar 

  50. Melendez, J., van Ginneken, B., Maduskar, P., Philipsen, R.H., Ayles, H., Sánchez, C.I.: On combining multiple-instance learning and active learning for computer-aided detection of tuberculosis. IEEE Trans. Med. Imaging 35(4), 1013–1024 (2016)

    Article  Google Scholar 

  51. Arzhaeva, Y., Hogeweg, L., de Jong, P. A., Viergever, M.A., van Ginneken, B.: Global and local multi-valued dissimilarity-based classification: application to computer-aided detection of tuberculosis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 724–731, Sept, 2009. Springer, Berlin, Heidelberg

    Google Scholar 

  52. Jaeger, S., Karargyris, A., Antani, S., Thoma, G.: Detecting tuberculosis in radiographs using combined lung masks. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4978–4981, Aug, 2012. IEEE

    Google Scholar 

  53. Jaeger, S., Antani, S., Thoma, G.: Tuberculosis screening of chest radiographs. In: SPIE Newsroom (2011)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, PEC University of Technology, Chandigarh, India

    Rahul Hooda & Sanjeev Sofat

  2. University Institute of Engineering and Technology, Panjab University, Chandigarh, India

    Ajay Mittal

Authors
  1. Rahul Hooda
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  2. Ajay Mittal
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  3. Sanjeev Sofat
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Corresponding author

Correspondence to Rahul Hooda .

Editor information

Editors and Affiliations

  1. Department of Physics, Amity School of Applied Sciences, Amity University Rajasthan, Jaipur, Rajasthan, India

    Kanad Ray

  2. Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, Rajasthan, India

    Tarun K. Sharma

  3. Department of Electronics and Communication Engineering, SEEC, Manipal University Jaipur, Jaipur, Rajasthan, India

    Sanyog Rawat

  4. Institute of Basic Science, Bundelkhand University, Jhansi, Uttar Pradesh, India

    R. K. Saini

  5. Advanced Key Technologies Division, Nano Characterization Unit, Surface Characterization Group, National Institute for Materials Science, Tsukuba, Ibaraki, Japan

    Anirban Bandyopadhyay

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Hooda, R., Mittal, A., Sofat, S. (2019). A Survey of CAD Methods for Tuberculosis Detection in Chest Radiographs. In: Ray, K., Sharma, T., Rawat, S., Saini, R., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 742. Springer, Singapore. https://doi.org/10.1007/978-981-13-0589-4_25

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