Advertisement

Chest X-ray segmentation using Sauvola thresholding and Gaussian derivatives responses

  • Mahreen Kiran
  • Imran Ahmed
  • Nazish Khan
  • Alavalapati Goutham ReddyEmail author
Original Research
  • 36 Downloads

Abstract

This paper presents a simple, flexible and an effective lung segmentation technique called ST-GD (Sauvola thresholding-Gaussian derivatives) method. In this technique Sauvola thresholding method and four Gaussian derivatives responses are used. This technique for extraction of lung field area is consist of six main steps. (1) For the purpose of enhancement the image is preprocessed. This is achieved by using adaptive contrast enhancement and normalization. (2) The average image is calculated from a Gaussian derivatives of four different magnitudes in such a way that it highlights the outer boundary of the lung region. (3) Preprocessed image is then thresholded by using Sauvola image thresholding which mostly highlights the inner area of the lung region. (4) To emphasize the lung region completely the Sauvola thresholded image and gradient average image is combined. (5) Once the image is combined, to remove the noisy area such as trachea, clavicle region and outer body, XOR is taken between similar X-rays average image and combined image. (6) Finally, morphology is used to remove the noise that has been occurred during the formation of lung shape. This developed system tested on JSRT, Montgomery and a self collected dataset. The self-collected database has been collected from Northwest General Hospital and Research Center, Peshawar, Pakistan. The proposed system produced an accuracy of 94.57% on JSRT dataset, 90.75% accuracy on Montgomery dataset and 65.25% on Northwest dataset using Jaccard coefficient. Furthermore, it is also investigated that the proposed study has outperformed as compared to the state-of-the-art methods.

Keywords

Segmentation Chest X-ray segmentation Sauvola thresholding Gaussian derivative Computer-aided diagnosis Lung region extraction 

Notes

Acknowledgements

The authors would like to thank the Radiology Department and especially Mr. Aijaz in Northwest General Hospital and Research center, Peshawar, Pakistan for providing the chest radiographs dataset and medical advice. Also, this work was supported by the faculty research fund of Sejong University during 2017–2018 and Seed Grant, National Institute of Technology Andhra Pradesh (2018–2020).

References

  1. Abdel-massieh NH (2012) Fully automatic technique for liver segmentation from abdominal CT scan with knowledge-based constraints. INTECH Open Access PublisherGoogle Scholar
  2. Ahmad WSHMW, Zaki WMDW, Fauzi MFA (2015) Lung segmentation on standard and mobile chest radiographs using oriented gaussian derivatives filter. Biomed Eng Online 14(1):1Google Scholar
  3. Ahmed I, Adnan A (2017) A robust algorithm for detecting people in overhead views. Cluster computing, pp 1–22Google Scholar
  4. Ahmed I, Ahmad A, Piccialli F, Sangaiah AK, Jeon G (2018) A robust features-based person tracker for overhead views in industrial environment. IEEE Internet Things J 5(3):1598–1605Google Scholar
  5. Al-Amri SS, Kalyankar NV et al (2010) Image segmentation by using threshold techniques. arXiv:1005.4020 (arXiv preprint) Google Scholar
  6. Alazab M, Islam M, Venkatraman S (2009) Towards automatic image segmentation using optimised region growing technique. In: Australasian joint conference on artificial intelligence. Springer, pp 131–139Google Scholar
  7. Annangi P, Thiruvenkadam S, Raja A, Xu H, Sun X, Mao L (2010) 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. IEEE, pp 892–895Google Scholar
  8. Antani S (2015) Automated detection of lung diseases in chest X-rays. US National Library of MedicineGoogle Scholar
  9. Anter AM, Azar AT, Hassanien AE, El-Bendary N, ElSoud MA (2013) Automatic computer aided segmentation for liver and hepatic lesions using hybrid segmentations techniques. In: 2013 Federated conference on computer science and information systems (FedCSIS). IEEE, pp 193–198Google Scholar
  10. Ashraf R, Ahmed M, Ahmad U, Habib MA, Jabbar S, Naseer K (2018a) MDCBIR-MF: multimedia data for content-based image retrieval by using multiple features. Multimedia tools and applications, pp 1–27Google Scholar
  11. Ashraf R, Ahmed M, Jabbar S, Khalid S, Ahmad A, Din S, Jeon G (2018b) Content based image retrieval by using color descriptor and discrete wavelet transform. J Med Syst 42(3):44Google Scholar
  12. Brown MS, Wilson LS, Doust BD, Gill RW, Sun C (1998) Knowledge-based method for segmentation and analysis of lung boundaries in chest X-ray images. Comput Med Imaging Graph 22(6):463–477Google Scholar
  13. Bueno S, Martinez-Albala A, Cosfas P (2004) Fuzziness and PDE based models for the segmentation of medical image. In: 2004 IEEE nuclear science symposium conference record, vol 6. IEEE, pp 3777–3780Google Scholar
  14. Candemir S, Jaeger S, Palaniappan K, Antani S, Thoma G (2012) Graph-cut based automatic lung boundary detection in chest radiographs. In: IEEE healthcare technology conference: translational engineering in health and medicine, pp 31–34Google Scholar
  15. Candemir S, Jaeger S, Palaniappan K, Musco JP, Singh RK, Xue Z, Karargyris A, Antani S, Thoma G, McDonald CJ (2014) Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans Med Imaging 33(2):577–590Google Scholar
  16. Chaki N, Shaikh SH, Saeed K (2014) A comprehensive survey on image binarization techniques. In: Exploring image binarization techniques. Springer, pp 5–15Google Scholar
  17. Coppini G, Miniati M, Monti S, Paterni M, Favilla R, Ferdeghini EM (2013) A computer-aided diagnosis approach for emphysema recognition in chest radiography. Med Eng Phys 35(1):63–73Google Scholar
  18. Dawoud A (2010a) Fusing shape information in lung segmentation in chest radiographs. In: International conference image analysis and recognition. Springer, pp 70–78Google Scholar
  19. Dawoud A (2010b) Fusing shape information in lung segmentation in chest radiographs. Springer, Berlin, pp 70–78Google Scholar
  20. ERS (2016) The burden of lung disease: European respiratory society, European lung white book. http://www.erswhitebook.org/chapters/the-burden-of-lung-disease/
  21. Hogeweg L, Sánchez CI, Maduskar P, Philipsen R, Story A, Dawson R, Theron G, Dheda K, Peters-Bax L, van Ginneken B (2015) Automatic detection of tuberculosis in chest radiographs using a combination of textural, focal, and shape abnormality analysis. IEEE Trans Med Imaging 34(12):2429–2442Google Scholar
  22. Iakovidis DK, Savelonas M (2009) Active shape model aided by selective thresholding for lung field segmentation in chest radiographs. In: 2009 9th international conference on information technology and applications in biomedicine. IEEE, pp 1–4Google Scholar
  23. Jaeger S, Karargyris A, Antani S, Thoma G (2012) Detecting tuberculosis in radiographs using combined lung masks. In: 2012 Annual international conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp 4978–4981Google Scholar
  24. Jaeger S, Karargyris A, Candemir S, Folio L, Siegelman J, Callaghan F, Xue Z, Palaniappan K, Singh RK, Antani S et al (2014) Automatic tuberculosis screening using chest radiographs. IEEE Trans Med Imaging 33(2):233–245Google Scholar
  25. Jamil U, Khalid S, Akram MU, Ahmad A, Jabbar S (2018) Melanocytic and nevus lesion detection from diseased dermoscopic images using fuzzy and wavelet techniques. Soft Comput 22(5):1577–1593Google Scholar
  26. Juhász S, Horváth Á, Nikházy L, Horváth G (2010) Segmentation of anatomical structures on chest radiographs. In: XII Mediterranean conference on medical and biological engineering and computing 2010. Springer, pp 359–362Google Scholar
  27. Karargyris A, Antani S, Thoma G (2011) Segmenting anatomy in chest X-rays for tuberculosis screening. In: 2011 Annual international conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp 359–362Google Scholar
  28. Khan N, Ahmed I, Kiran M, Adnan A (2016) Overview of technical elements of liver segmentation. Int J Adv Comput Sci Appl 7(12):271–278Google Scholar
  29. Kumar S, Moni R, Rajeesh J (2011) Automatic segmentation of liver and tumor for cad of liver. J Adv Inf Technol 2(1):63–70Google Scholar
  30. Kyu HH, Stein CE, Pinto CB, Rakovac I, Weber MW, Purnat TD, Amuah JE, Glenn SD, Cercy K, Biryukov S et al (2018) Causes of death among children aged 5–14 years in the who european region: a systematic analysis for the global burden of disease study 2016. Lancet Child Adolesc Health 2(5):321–337Google Scholar
  31. Le K (2011) A design of a computer-aided diagnostic tool for chest X-ray analysis. Int J Comput Sci Inf Technol 3(2):212–222Google Scholar
  32. Li H, Elmoataz A, Fadili JM, Ruan S (2003) An improved image segmentation approach based on level set and mathematical morphology. In: Third international symposium on multispectral image processing and pattern recognition. International Society for Optics and Photonics, pp 851–854Google Scholar
  33. Li X, Luo S, Hu Q, Li J, Wang D, Chiong F (2016) Automatic lung field segmentation in X-ray radiographs using statistical shape and appearance models. J Med Imaging Health Inform 6(2):338–348Google Scholar
  34. Melendez J, van Ginneken B, Maduskar P, Philipsen RH, Reither K, Breuninger M, Adetifa IM, Maane R, Ayles H, Sánchez CI (2015) A novel multiple-instance learning-based approach to computer-aided detection of tuberculosis on chest X-rays. IEEE Trans Med Imaging 34(1):179–192Google Scholar
  35. Narayanan B (2015) A novel computer aided detection of identifying lung nodules on chest radiographsGoogle Scholar
  36. Ngo TA, Carneiro G (2015) Lung segmentation in chest radiographs using distance regularized level set and deep-structured learning and inference. In: 2015 IEEE international conference on image processing (ICIP). IEEE, pp 2140–2143Google Scholar
  37. Niblack W (1985) An introduction to digital image processing. Strandberg Publishing Company, CopenhagenGoogle Scholar
  38. Noor NM, Than JC, Rijal OM, Kassim RM, Yunus A, Zeki AA, Anzidei M, Saba L, Suri JS (2015) Automatic lung segmentation using control feedback system: morphology and texture paradigm. J Med Syst 39(3):1–18Google Scholar
  39. Pham DL, Xu C, Prince JL (2000) Current methods in medical image segmentation 1. Annu Rev Biomed Eng 2(1):315–337Google Scholar
  40. Pizer SM, Johnston RE, Ericksen JP, Yankaskas BC, Muller KE (1990) Contrast-limited adaptive histogram equalization: speed and effectiveness. In: Proceedings of the first conference on visualization in biomedical computing. IEEE, pp 337–345Google Scholar
  41. Rossi F, Rahni AAA (2015) Combination of low level processing and active contour techniques for semi-automated volumetric lung lesion segmentation from thoracic ct images. In: 2015 IEEE student symposium in biomedical engineering and sciences (ISSBES). IEEE, pp 26–30Google Scholar
  42. 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: 2014 IEEE international conference on control system, computing and engineering (ICCSCE). IEEE, pp 46–51Google Scholar
  43. Sauvola J, Pietikäinen M (2000) Adaptive document image binarization. Pattern Recognit 33(2):225–236Google Scholar
  44. Seghers D, Loeckx D, Maes F, Vandermeulen D, Suetens P (2007) Minimal shape and intensity cost path segmentation. IEEE Trans Med Imaging 26(8):1115–1129Google Scholar
  45. Shi Y, Qi F, Xue Z, Chen L, Ito K, Matsuo H, Shen D (2008) Segmenting lung fields in serial chest radiographs using both population-based and patient-specific shape statistics. IEEE Trans Med Imaging 27(4):481–494Google Scholar
  46. Soleymanpour E, Pourreza HR et al (2011) Fully automatic lung segmentation and rib suppression methods to improve nodule detection in chest radiographs. J Med Signals Sens 1(3):191Google Scholar
  47. Soliman A, Elnakib A, Khalifa F, El-Ghar MA, El-Baz A (2015) Segmentation of pathological lungs from CT chest images. In: 2015 IEEE international conference on image processing (ICIP). IEEE, pp 3655–3659Google Scholar
  48. Sreeja Mole S, Aiswarya A, Akhila L, Akhila S (2015) Automated approach for detecting tuberculosis using chest radiographsGoogle Scholar
  49. Suzuki K, Kohlbrenner R, Epstein ML, Obajuluwa AM, Xu J, Hori M (2010) Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms. Med Phys 37(5):2159–2166Google Scholar
  50. Van Ginneken B, ter Haar Romeny BM (2000) Automatic segmentation of lung fields in chest radiographs. Med Phys 27(10):2445–2455Google Scholar
  51. Van Ginneken B, Romeny BTH, Viergever MA (2001) Computer-aided diagnosis in chest radiography: a survey. IEEE Trans Med Imaging 20(12):1228–1241Google Scholar
  52. Van Ginneken B, Frangi AF, Staal JJ, ter Haar Romeny BM, Viergever MA (2002) Active shape model segmentation with optimal features. IEEE Trans Med Imaging 21(8):924–933Google Scholar
  53. 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):19–40Google Scholar
  54. Verma KK, Kumar P, Tomar A, Srivastava M (2015) A comparative study of image segmentation techniques in digital image processingGoogle Scholar
  55. Wu G, Zhang X, Luo S, Hu Q (2015) Lung segmentation based on customized active shape model from digital radiography chest images. J Med Imaging Health Inform 5(2):184–191Google Scholar
  56. Xu T, Cheng I, Mandal M (2011) Automated cavity detection of infectious pulmonary tuberculosis in chest radiographs. In: 2011 Annual international conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp 5178–5181Google Scholar
  57. Xu T, Mandal M, Long R, Cheng I, Basu A (2012) An edge-region force guided active shape approach for automatic lung field detection in chest radiographs. Comput Med Imaging Graph 36(6):452–463Google Scholar
  58. Yu T, Luo J, Ahuja N (2005) Shape regularized active contour using iterative global search and local optimization. In: 2005 IEEE Computer Society conference on computer vision and pattern recognition (CVPR’05), vol 2. IEEE, pp 655–662Google Scholar
  59. Zafar B, Ashraf R, Ali N, Ahmed M, Jabbar S, Naseer K, Ahmad A, Jeon G (2018) Intelligent image classification-based on spatial weighted histograms of concentric circles. Comput Sci Inf Syst 15(3):615–633Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Center of Excellence in Information Technology, Institute of Management SciencesPeshawarPakistan
  2. 2.Department of Computer and Information SecuritySejong UniversitySeoulSouth Korea
  3. 3.Department of Computer Science and EngineeringNational Institute of Technology-Andhra PradeshTadepalligudemIndia

Personalised recommendations