Comparative analysis of segmentation techniques based on chest X-ray images


The image segmentation is the basic step in the image processing involved in the processing of medical images. Over the past two decades, medical image segmentation has remained a challenge for researchers while the use of this imaging modality is rapidly growing in research studies. This article surveys the techniques and their effect on chest X-ray images. The objective of this work is to study the key similarities and differences among the different published methods while highlighting their strengths and weaknesses on chest X-ray images. The reason is to assist the researchers in the choice of an appropriate lung segmentation methodology. We additionally give a complete portrayal of the existing few basic methods when combined with preprocessing method that can be utilized as a part of the segmentation. A discussion and fair analysis justified with experimental results along with quantitative correlation of the outcomes on 247 images of JSRT through Dice coefficient exhibited.

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  1. 1.


  1. 1.

    Anand (2013) Similarity measure. Accepted Answer from Accessed 26 March 2013

  2. 2.

    Antani S (2015) Automated detection of lung diseases in chest x-rays. US National Library of Medicine, Bethesda

    Google Scholar 

  3. 3.

    Atlas based lung segmentation (2016) Accessed 27 Jan 2016

  4. 4.

    Bradley D, Roth G (2007) Adaptive thresholding using the integral image. Journal of Graphics, GPU, and Game Tools 12(2):13–21

    Article  Google Scholar 

  5. 5.

    Bueno S, Martinez-Albala A, Cosfas P (2004) Fuzziness and pde based models for the segmentation of medical image. In Nuclear Science Symposium Conference Record, 2004 IEEE, volume 6, pages 3777–3780. IEEE

  6. 6.

    Camilus S (2009) Fuzzy c-means segmentation. Available from Accessed 09 Oct 2009

  7. 7.

    Chaki N, Shaikh SH, Saeed K (2014) A comprehensive survey on image binarization techniques. In Exploring Image Binarization Techniques 5–15

  8. 8.

    Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277

    MATH  Article  Google Scholar 

  9. 9.

    Cohen LD (1991) On active contour models and balloons. CVGIP: Image Understanding 53(2):211–218

    MATH  Article  Google Scholar 

  10. 10.

    Contrast enhancement techniques (2017). Accessed 07 Feb 2017

  11. 11.

    Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302

    Article  Google Scholar 

  12. 12.

    Dunn JC (1973) A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters

  13. 13.

    Feldman MJ, Hoffer EP, Barnett GO, Kim RJ, Famiglietti KT, Chueh HC (2012) Impact of a computer-based diagnostic decision support tool on the differential diagnoses of medicine residents. Journal of Graduate Medical Education 4(2):227–231

    Article  Google Scholar 

  14. 14.

    Felzenszwalb PF, Huttenlocher DP (2004) Efficient graphbased image segmentation. Int J Comput Vis 59(2):167–181

    Article  Google Scholar 

  15. 15.

    Garcia D (2010) Image segmentation using otsu thresholding. Available from Accessed 10 Mar 2010

  16. 16.

    Ghosh S, Dubey SK (2013) Comparative analysis of k-means and fuzzy c-means algorithms. Int J Adv Comput Sci Appl 4(4)

  17. 17.

    Histogram equalization (2015) Available from: Accessed 09 Apr 2015

  18. 18.

    Bezdek JC (2013) Pattern recognition with fuzzy objective function algorithms. Springer Science & Business Media, New York

    Google Scholar 

  19. 19.

    K means image segmentation in matlab (2007). Available from Accessed 27 Aug 2007

  20. 20.

    Kamra P, Vishraj R, Gupta S et al (2015) Performance comparison of image segmentation techniques for lung nodule detection in ct images. In Signal Processing, Computing and Control (ISPCC), 2015 International Conference on, pages 302–306. IEEE

  21. 21.

    Kass M, Witkin A, Terzopoulos D (1988) Snakes: Active contour models. Int J Comput Vis 1(4):321–331

    MATH  Article  Google Scholar 

  22. 22.

    Kato Z. Zerubia J et al (2012) Markov random fields in image segmentation. Foundations and Trends® in Signal Processing 5(1–2):1–155

  23. 23.

    Kaur N, Kaur R (2011) A review on various methods of image thresholding. International Journal on Computer Science and. Engineering 3(10):3441

    Google Scholar 

  24. 24.

    Khan A, Ravi S (2013) Image segmentation methods: A comparative study

  25. 25.

    Kim J-Y, Kim L-S, Hwang S-H (2001) An advanced contrast enhancement using partially overlapped sub-block histogram equalization. IEEE Transactions on Circuits and Systems for Video Technology 11(4):475–484

    Article  Google Scholar 

  26. 26.

    Lankton S (2016) Active contour segmentation. Available from Accessed 31 Mar 2016

  27. 27.

    Lee J, Pant SR, Lee H-S (2015) An adaptive histogram equalization based local technique for contrast preserving image enhancement. International Journal of Fuzzy Logic and Intelligent Systems 15(1):35–44

    Article  Google Scholar 

  28. 28.

    Lim SH, Isa NAM, Ooi CH, Toh KKV (2015) A new histogram equalization method for digital image enhancement and brightness preservation. SIViP 9(3):675–689

    Article  Google Scholar 

  29. 29.

    Lin (2015) Image segmentation based on Markov random fields. Available from Accessed 02 Nov 2015

  30. 30.

    Magudeeswaran V, Ravichandran C (2013) Fuzzy logic-based histogram equalization for image contrast enhancement. Math Probl Eng 2013

  31. 31.

    Marker-controlled watershed segmentation. Available from

  32. 32.

    MathWorks. Graythresh (2016) Available from Accessed 04 Aug 2016

  33. 33.

    Moftah HM, Azar AT, Al-Shammari ET, Ghali NI, Hassanien AE, Shoman M (2014) Adaptive k-means clustering algorithm for mr breast image segmentation. Neural Comput & Applic 24(7-8):1917–1928

    Article  Google Scholar 

  34. 34.

    Motl J (2013) Niblack. Available from Accessed 10 May 2013

  35. 35.

    Motl J (2013) Sauvola local image thresholding. Available from Accessed 08 May 2013

  36. 36.

    Motl J (2015) Bradley local image thresholding. Available from Accessed 19 Apr 2015

  37. 37.

    Nair DMS (2007) Edge detection and segmentation. Available from Accessed 11 Jan 2007

  38. 38.

    Niblack W (1986) An introduction to digital image processing. Prentice Hall, Englewood Cliffs

    Google Scholar 

  39. 39.

    P. Orchard (2007) Markov random field optimisation. Available from:

  40. 40.

    Otsu N (1975) A threshold selection method from gray-level histograms. Automatica 11(285-296):23–27

    Google Scholar 

  41. 41.

    Pham DL, Xu C, Prince JL (2000) Current methods in medical image segmentation 1. Annu Rev Biomed Eng 2(1):315–337

    Article  Google Scholar 

  42. 42.

    Rais NB, Hanif MS, Taj IA (2004) Adaptive thresholding technique for document image analysis. In Multitopic Conference, 2004. Proceedings of INMIC 2004. 8th International, pages 61–66. IEEE

  43. 43.

    Saad MN, Muda Z, Ashaari NS, Hamid HA (2014) Image segmentation for lung region in chest x-ray images using edge detection and morphology. In Control System, Computing and Engineering (ICCSCE), 2014 IEEE International Conference on, pages 46–51. IEEE

  44. 44.

    Sauvola J, Pietikäinen M (2000) Adaptive document image binarization. Pattern Recogn 33(2):225–236

    Article  Google Scholar 

  45. 45.

    Segmentation using clustering methods. Available from

  46. 46.

    Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905

    Article  Google Scholar 

  47. 47.

    Shoelson B (2016) Thresholdlocally. Available from Accessed 01 Sep 2016

  48. 48.

    Starck J-L, Murtagh F, Candes EJ, Donoho DL (2003) Gray and color image contrast enhancement by the curvelet transform. IEEE Trans Image Process 12(6):706–717

    MathSciNet  MATH  Article  Google Scholar 

  49. 49.

    Stark JA (2000) Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Trans Image Process 9(5):889–896

    Article  Google Scholar 

  50. 50.

    Stolojescu-Cri C (2013) ¸San and ¸S. Holban. A comparison of x-ray image segmentation techniques. Advances in Electrical and Computer Engineering. Engineering 13(3)

  51. 51.

    Subashini P, Sridevi N (2011) An optimal binarization algorithm based on particle swarm optimization. International Journal of Soft Computing and Engineering (IJSCE) 1

  52. 52.

    Suvanov S, Choi J-J. Contrast enhancement using sub-image histogram equalization

  53. 53.

    Ting C-C, Wu B-F, Chung M-L, Chiu C-C, Wu Y-C (2015) Visual contrast enhancement algorithm based on histogram equalization. Sensors 15(7):16981–16999

    Article  Google Scholar 

  54. 54.

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

    Article  Google Scholar 

  55. 55.

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

    Article  Google Scholar 

  56. 56.

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

    Article  Google Scholar 

  57. 57.

    Vicente S, Kolmogorov V, Rother C (2008) Graph cut based image segmentation with connectivity priors. In Computer vision and pattern recognition, 2008. CVPR 2008. IEEE conference on, pages 1–8. IEEE

  58. 58.

    Wong LP (2007) Sauvola local image thresholding. Available from Accessed 09 Feb 2007

  59. 59.

    Wong L, Ewe H (2005) A study of lung cancer detection using chest x-ray images. In Proceedings of 3rd APT Telemedicine Workshop, Kuala Lumpur, pages 27–28

  60. 60.

    Xu H, Chen Q, Zuo C, Yang C, Liu N (2015) Range limited double-thresholds multi-histogram equalization for image contrast enhancement. Opt Rev 22(2):246–255

    Article  Google Scholar 

  61. 61.

    Yao H, Duan Q, Li D, Wang J (2013) An improved k-means clustering algorithm for fish image segmentation. Math Comput Model 58(3):790–798

    MATH  Article  Google Scholar 

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Correspondence to Alavalapati Goutham Reddy.

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Kiran, M., Ahmed, I., Khan, N. et al. Comparative analysis of segmentation techniques based on chest X-ray images. Multimed Tools Appl 79, 8483–8518 (2020).

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  • Chest radiography
  • Survey
  • Computer-aided diagnosis
  • Codes
  • executable
  • Commands
  • Lung region extraction
  • Segmentation