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access icon free Lung segmentation based on random forest and multi-scale edge detection

  • Author(s): Caixia Liu 1, 2 ; Ruibin Zhao 1, 3 ; Mingyong Pang 1
    • View affiliations
  • Source: Volume 13, Issue 10, 22 August 2019, p. 1745 – 1754
    DOI:  10.1049/iet-ipr.2019.0130 , Print ISSN 1751-9659, Online ISSN 1751-9667
© The Institution of Engineering and Technology
Received 26/01/2019, Accepted 28/05/2019, Revised 03/05/2019, Published 04/06/2019

To achieve an automatic and accurate segmentation of lungs and improve the clinical efficiency of computer-aided diagnosis, the authors present a lung segmentation algorithm based on the random forest method and a multi-scale edge detection technique. The algorithm carries a first step of lung region extraction and a second step of lung nodule segmentation. By combining texture information, the improved superpixel generation method can better deal with initial segmentation on lung computed tomography images with inhomogeneous intensity. Then, the lung region is further extracted by using the random forest classifier on the superpixel features, and the lung contours are corrected with a proposed circle tracing technique. Finally, the segmentation is further refined by employing a multi-scale edge detection technique, which enables their method to detect suspicious nodules with various intensities and sizes adaptively. The effectiveness of the proposed approach is demonstrated on a group of datasets by comparing with the corresponding ground truths as well as the classical algorithms. Experimental results show that the proposed method has a higher precision than the compared algorithms in a fully automatic fashion.

Inspec keywords: feature extraction; medical image processing; image texture; edge detection; image segmentation; lung; computerised tomography; image classification

Other keywords: circle tracing technique; random forest classifier; multiscale edge detection technique; computer-aided diagnosis; improved superpixel generation method; lung computed tomography images; initial segmentation; accurate segmentation; lung nodule segmentation; lung region extraction; lung contours; automatic segmentation; random forest method; lungs; lung segmentation algorithm

Subjects: Computer vision and image processing techniques; X-rays and particle beams (medical uses); Image recognition; X-ray techniques: radiography and computed tomography (biomedical imaging/measurement); Patient diagnostic methods and instrumentation; Optical, image and video signal processing; Biology and medical computing

References

    1. 1)
      • 39. Lewis, D.: ‘Evaluating text categorization’. Proc. Int. Conf. Workshop on Speech and Natural Language, California, USA, 1991, pp. 312–318.
    2. 2)
      • 17. Peng, T., Wang, Y., Xu, T.C., et al: ‘Detection of lung contour with closed principal curve and machine learning’, J. Digit. Imaging, 2018, 31, (4), pp. 520–533.
    3. 3)
      • 12. Sunil, K., Ashok, K.: ‘Lung segmentation using region growing algorithm’, Int. J. Adv. Res. Comput. Sci. Softw. Eng., 2014, 4, (3), pp. 184–187.
    4. 4)
      • 18. Jalal, D., Ganesan, R., Merline, A.: ‘Fuzzy-C-means clustering based segmentation and CNN-classification for accurate segmentation of lung nodules’, Asian Pac. J. Cancer Prev., 2017, 18, (7), pp. 1869–1874.
    5. 5)
      • 27. Armato, S., Giger, M., Doi, K., et al: ‘Computerized lung nodule detection: comparison of performance for low-dose and standard-dose helical CT scans’. Proc. Int. Conf. Society for Optical Engineering, San Diego, USA, 2001, vol. 4322, no. 3, pp. 1449–1454.
    6. 6)
      • 32. Turkowski, K.: ‘Filters for common resampling tasks’, Graph. Gems, 1990, 16, (3), pp. 147–165.
    7. 7)
      • 14. Nithila, E.E., Kumar, S.S.: ‘Segmentation of lung nodule in CT data using active contour model and fuzzy C-mean clustering’, Alexandria Eng. J., 2016, 55, (3), pp. 2583–2588.
    8. 8)
      • 33. Depeursinge, A., Vargas, A., Platon, A., et al: ‘Building a reference multimedia database for interstitial lung diseases’, Comput. Med. Imaging Graph., 2012, 36, (3), pp. 227–238.
    9. 9)
      • 19. Xu, M., Qi, S., Yue, Y., et al: ‘Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset’, Biomed. Eng. Online, 2019, 18, (1), pp. 1–21.
    10. 10)
      • 31. Al-Kadi, O.S., Watson, D.: ‘Texture analysis of aggressive and nonaggressive lung tumor CE CT images’, IEEE Trans. Biomed. Eng., 2008, 55, (7), pp. 1822–1830.
    11. 11)
      • 20. Huang, Z., Li, Q., Zhang, T., et al: ‘Iterative weighted sparse representation for X-ray cardiovascular angiogram image denoising over learned dictionary’, IET Image Process., 2018, 12, (2), pp. 254–261.
    12. 12)
      • 9. John, J., Mini, M.G.: ‘Multilevel thresholding based segmentation and feature extraction for pulmonary nodule detection’, Procedia Technol., 2016, 24, pp. 957–963.
    13. 13)
      • 1. Firmino, M., Morais, A.H., Mendoça, R.M., et al: ‘Computer-aided detection system for lung cancer in computed tomography scans: review and future prospects’, Biomed. Eng. Online, 2014, 13, (1), pp. 41–56.
    14. 14)
      • 29. Shen, S., Bui, A., Cong, J., et al: ‘An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy’, Comput. Biol. Med., 2015, 57, pp. 139–149.
    15. 15)
      • 24. Arbeláez, P., Maire, M., Fowlkes, C., et al: ‘Contour detection and hierarchical image segmentation’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, (5), pp. 898–916.
    16. 16)
      • 2. Lu, J., Li, Z., Han, Y.: ‘The lung cancer research progress in China’, Jilin Med. Inf., 2006, 23, (Z2), p. 2.
    17. 17)
      • 11. Naqi, S.M., Sharif, M., Yasmin, M.: ‘Multistage segmentation model and SVM-ensemble for precise lung nodule detection’, Int. J. Comput. Assist. Radiol. Surg., 2018, 13, (7), pp. 1083–1095.
    18. 18)
      • 3. Peroš-Golubičić, T.M., Frcp, O.: ‘Clinical atlas of interstitial lung disease’, Respir. Care, 2007, 52, (12), pp. 1784–1784.
    19. 19)
      • 8. Javaid, M., Javid, M., Rehman, M.Z.U., et al: ‘A novel approach to CAD system for the detection of lung nodules in CT images’, Comput. Methods Programs Biomed., 2016, 135, pp. 125–139.
    20. 20)
      • 15. Naseri Samaghcheh, Z., Abdoli, F., Abrishami Moghaddam, H., et al: ‘A new model-based framework for lung tissue segmentation in three-dimensional thoracic CT images’, Signal Image Video Process., 2018, 12, (2), pp. 339–346.
    21. 21)
      • 30. Sluimer, I., Schilham, A., Prokop, M., et al: ‘Computer analysis of computed tomography scans of the lung: a survey’, IEEE Trans. Med. Imaging, 2006, 25, (4), pp. 385–405.
    22. 22)
      • 21. Rundo, L., Tangherloni, A., Nobile, M.S., et al: ‘MedGA: a novel evolutionary method for image enhancement in medical imaging systems’, Expert Syst. Appl., 2019, 119, pp. 387–399.
    23. 23)
      • 16. Prakash Sahu, S., Agrawal, P., Londhe, N.D., et al: ‘A new hybrid approach using fuzzy clustering and morphological operations for lung segmentation in thoracic CT images’, Biomed. Pharmacol. J., 2017, 10, (4), pp. 1949–1961.
    24. 24)
      • 34. Armato, S.G., Mclennan, G., Bidaut, L., et al: ‘The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans’, Acad. Radiol., 2007, 14, (12), pp. 1455–1463.
    25. 25)
      • 6. van Rikxoort, E.M., de Hoop, B., Viergever, M.A., et al: ‘Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection’, Med. Phys., 2009, 36, (7), pp. 2934–2947.
    26. 26)
      • 7. Lee, S.L.A., Kouzani, A.Z., Hu, E.J.: ‘Automated detection of lung nodules in computed tomography images: a review’, Mach. Vis. Appl., 2012, 23, (1), pp. 151–163.
    27. 27)
      • 13. Zhao, J., Ji, G., Qiang, Y., et al: ‘A new method of detecting pulmonary nodules with PET/CT based on an improved watershed algorithm’, PLoS One, 2015, 10, (4), pp. 1–15.
    28. 28)
      • 4. Sharman, P., Wood-Baker, R.: ‘Interstitial lung disease due to fumes from heat-cutting polymer rope’, Occup. Med., 2013, 63, (6), pp. 451–453.
    29. 29)
      • 23. Achanta, R., Shaji, A., Smith, K., et al: ‘SLIC superpixels compared to state-of-the-art superpixel methods’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (11), pp. 2274–2282.
    30. 30)
      • 10. Mansoor, A., Bagci, U., Mollura, D.: ‘Near-optimal keypoint sampling for fast pathological lung segmentation’. Proc. Int. Conf. IEEE Engineering in Medicine and Biology Society, Chicago, USA, 2014, pp. 6032–6035.
    31. 31)
      • 36. Chen, S., Zhang, D.: ‘Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure’, IEEE Trans. Syst. Man Cybern. B, Cybern., 2004, 34, (4), pp. 1907–1916.
    32. 32)
      • 38. Krinidis, S., Chatzis, V.: ‘A robust fuzzy local information C-means clustering algorithm’, IEEE Trans. Image Process., 2010, 19, (5), pp. 1328–1337.
    33. 33)
      • 26. Zhao, J., Ma, R., Qiang, Y., et al: ‘Solitary pulmonary nodule segmentation based on the rolling ball method’, J. Comput. Theor. Nanosci., 2015, 12, (8), pp. 1977–1983.
    34. 34)
      • 25. Breiman, L.: ‘Random forests’, Mach. Learn., 2001, 45, (1), pp. 5–32.
    35. 35)
      • 5. Santos, A.M., de Carvalho Filho, A.O., Silva, A.C., et al: ‘Automatic detection of small lung nodules in 3D CT data using Gaussian mixture models, Tsallis entropy and SVM’, Eng. Appl. Artif. Intell., 2014, 36, pp. 27–39.
    36. 36)
      • 35. Liao, X., Zhao, J., Jiao, C., et al: ‘A segmentation method for lung parenchyma image sequences based on superpixels and a self-generating neural forest’, PLoS One, 2016, 11, (8), pp. 1–25.
    37. 37)
      • 37. Cai, W., Chen, S., Zhang, D.: ‘Fast and robust fuzzy C-means clustering algorithms incorporating local information for image segmentation’, Pattern Recognit., 2007, 40, (3), pp. 825–838.
    38. 38)
      • 22. Huang, Z., Zhang, Y., Li, Q., et al: ‘Spatially adaptive denoising for X-ray cardiovascular angiogram images’, Biomed. Signal Proc. Control, 2018, 40, pp. 131–139.
    39. 39)
      • 28. Gurcan, M., Sahiner, B., Petrick, N., et al: ‘Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system’, Med. Phys., 2002, 29, (11), pp. 2552–2558.
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