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On using a Particle Image Velocimetry based approach for candidate nodule detection

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Abstract

Detection and segmentation of candidate lung nodules from diagnostic images are vital steps in any image processing-based Computer-Aided Diagnostic (CAD) system for lung cancer. Computed Tomography (CT) is a commonly used modality for lung cancer screening due to the tissue contrast and anatomical resolution. This work aims to investigate the effectiveness of Particle Image Velocimetry, PIV, as a preprocessing tool for processing the input data frames. This is done by applying PIV processing to the input images and quantifying the nodules detected over a morphology-based image processing pipeline. Further, PIV processed images and images without PIV processing were input to the Convolution-based deep learning framework, and the candidate nodule detection effect was quantified and compared. The results validate the efficacy of the proposed workflow for candidate nodule detection both in the image processing pipeline and in the deep learning-based framework. Further, the work also presents the utility of the proposed preprocessing scheme through its ability to detect candidate nodules comprising the major nodule types, namely juxta-pleural, juxta-vascular, isolated, and ground-glass opacity nodules.

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Data Availability

The datasets generated during and/or analysed during the current study are available in the LIDC-IDRI repository, https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI.

Code Availability

The code for this work is available in https://github.com/sujijenkin/PIV_Morph and https://github.com/sujijenkin/PIV_UNetrepositories.

References

  1. Abe H (2018) Echocardiographic particle image velocimetry in heart diseases. In: 2018 IEEE international ultrasonics symposium (IUS), pp 1–1. IEEE

  2. Adrian L, Adrian RJ, Westerweel J (2011) Particle image velocimetry. 30 Cambridge University Press

  3. Aresta G, Cunha A, Campilho A (2017) Detection of juxta-pleural lung nodules in computed tomography images. In: Medical imaging 2017: computer-aided diagnosis, vol 10134, pp 101343N. International Society for Optics and Photonics

  4. Armato III SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz D, Biancardi AM, Bland PH, Brown MS, Engelmann RM, Laderach GE, Max D, Pais RC, Qing DPY, Roberts RY, Smith AR, Starkey A, Batrah P, Caligiuri P, Farooqi A, Gladish GW, Jude CM, Munden RF, Petkovska I, Quint LE, Schwartz LH, Sundaram B, Dodd LE, Fenimore C, Gur D, Petrick N, Freymann J, Kirby J, Hughes B, Casteele AV, Gupte S, Sallamm M, Heath MD, Kuhn MH, Dharaiya E, Burns R, Fryd DS, Salganicoff M, Anand V, Shreter U, Vastagh S, Croft BY, Clarke LP (2011) The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans. Medical physics 38(2):915–931

    Article  Google Scholar 

  5. Badura P, Pitka E (2008) Pre-and postprocessing stages in fuzzy connectedness-based lung nodule cad. In: Information technologies in biomedicine, pp 192–199. Springer

  6. Barbu I, Herzet C, Mémin E. (2011) Sparse models and pursuit algorithms for piv tomography. In: Forum on recent developments in volume reconstruction techniques applied to 3D fluid and solid mechanics

  7. Cavalcanti PG, Shirani S, Scharcanski J, Fong C, Meng J, Castelli J, Koff D (2016) Lung nodule segmentation in chest computed tomography using a novel background estimation method. Quant Imaging Med Surg 6(1):16

    Google Scholar 

  8. Dhara AK, Mukhopadhyay S, Khandelwal N (2012) Computer-aided detection and analysis of pulmonary nodule from ct images: a survey. IETE Tech Rev 29(4):265–275

    Article  Google Scholar 

  9. Edwards M (2021) Adaptive sampling in particle image velocimetry. Ph.D. thesis, University of Bristol

  10. Farnebäck G (2003) Two-frame motion estimation based on polynomial expansion. In: Scandinavian conference on image analysis, pp 363–370. Springer

  11. Firmino M, Angelo G, Morais H, Dantas MR, Valentim R (2016) Computer-aided detection (cade) and diagnosis (cadx) system for lung cancer with likelihood of malignancy. Biomed Eng Online 15(1):2

    Article  Google Scholar 

  12. Garcia-Duitama J, Chayer B, Goussard Y, Cloutier G (2016) Segmentation of blood layers with particle image velocimetry (piv) for reproducible in vivo characterization of erythrocyte aggregation. In: 2016 IEEE international ultrasonics symposium (IUS), pp 1–4. IEEE

  13. Gonçalves L, Novo J, Campilho A (2016) Hessian based approaches for 3d lung nodule segmentation. Expert Syst Appl 61:1–15

    Article  Google Scholar 

  14. Horn BK, Schunck BG (1981) Determining optical flow. Artif Intell 17(1-3):185–203

    Article  MATH  Google Scholar 

  15. Jacobs C, van Rikxoort EM, Twellmann T, Scholten ET, de Jong PA, Kuhnigk JM, Oudkerk M, de Koning HJ, Prokop M, Schaefer-Prokop C, Van Ginneken B (2014) Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images. Med Image Anal 18(2):374–384

    Article  Google Scholar 

  16. Javaid M, Javid M, Rehman MZU, Shah SIA (2016) A novel approach to cad system for the detection of lung nodules in ct images. Comput Methods Prog Biomed 135:125–139

    Article  Google Scholar 

  17. Joshi SR (2009) Improvement of algorithm in the particle tracking velocimetry using self-organizing maps. J Inst Eng 7(1):6–23

    Article  Google Scholar 

  18. Lucas BD (1981) Kanade T.: An iterative image registration technique with an application to stereo vision, vol 81

  19. Mansoor A, Bagci U, Foster B, Xu Z, Papadakis GZ, Folio LR, Udupa JK, Mollura DJ (2015) Segmentation and image analysis of abnormal lungs at ct: current approaches, challenges, and future trends. RadioGraphics 35(4):1056–1076

    Article  Google Scholar 

  20. Mukhopadhyay S (2016) A segmentation framework of pulmonary nodules in lung ct images. J Digit Imaging 29(1):86–103

    Article  Google Scholar 

  21. Nagargoje M (2017) An introduction to particle image velocimetry technique

  22. Naqi SM, Sharif M, Yasmin M (2018) Multistage segmentation model and svm-ensemble for precise lung nodule detection. Int J CARS 13(7):1083–1095

    Article  Google Scholar 

  23. Nithila EE, Kumar S (2019) Segmentation of lung from ct using various active contour models. Biomed Signal Process Control 47:57–62

    Article  Google Scholar 

  24. Olejniczak KJ (2000) The hartley transform. The Transforms and Applications Handbook, pp 281–330

  25. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 234–241. Springer

  26. Saien S, Pilevar AH, Moghaddam HA (2014) Refinement of lung nodule candidates based on local geometric shape analysis and laplacian of gaussian kernels. Comput Biol Med 54:188–198

    Article  Google Scholar 

  27. Schröder A, Willert CE (2008) Particle image velocimetry: new developments and recent applications

  28. Schultheiss M, Schober SA, Lodde M, Bodden J, Aichele J, Müller-Leisse C., Renger B, Pfeiffer F, Pfeiffer D (2020) A robust convolutional neural network for lung nodule detection in the presence of foreign bodies. Sci Rep 10(1):1–9

    Article  Google Scholar 

  29. Setio AA, Jacobs C, Gelderblom J, van Ginneken B (2015) Automatic detection of large pulmonary solid nodules in thoracic ct images. Med Phys 42(10):5642–5653

    Article  Google Scholar 

  30. Setio AAA, Traverso A, De Bel T, Berens MS, Van Den Bogaard C, Cerello P, Chen H, Dou Q, Fantacci ME, Geurts B et al (2017) Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the luna16 challenge. Med Image Anal 42:1–13

    Article  Google Scholar 

  31. Shaukat F, Raja G, Gooya A, Frangi AF (2017) Fully automatic detection of lung nodules in ct images using a hybrid feature set. Med Phys 44(7):3615–3629

    Article  Google Scholar 

  32. Suji RJ, Bhadouria SS, Dhar J, Godfrey WW (2019) Optical flow based background subtraction method for lung nodule segmentation. In: International conference on computer vision and image processing, pp 261–269. Springer

  33. Suji RJ, Bhadouria SS, Dhar J, Godfrey WW (2020) Optical flow methods for lung nodule segmentation on lidc-idri images. J Digit Imaging 33(5):1306–1324

    Article  Google Scholar 

  34. Tarashima S, Tange M, Someya S, Okamoto K, et al. (2010) Gpu accelerated direct cross-correlation piv with window deformation. In: Proceedings 15th int symp on applications of laser techniques to fluid mechanics

  35. Voorneveld J, Keijzer LB, Strachinaru M, Bowen DJ, Goei JS, Ten Cate F, van der Steen AF, de Jong N, Vos HJ, van den Bosch AE, Bosch JG (2019) High-frame-rate echo-particle image velocimetry can measure the high-velocity diastolic flow patterns, vol 12

  36. Wang S, Zhou M, Gevaert O, Tang Z, Dong D, Liu Z, Jie T (2017) A multi-view deep convolutional neural networks for lung nodule segmentation. In: 2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 1752–1755. IEEE

  37. Wang S, Zhou M, Liu Z, Liu Z, Gu D, Zang Y, Dong D, Gevaert O, Tian J (2017) Central focused convolutional neural networks: developing a data-driven model for lung nodule segmentation. Med Image Anal 40:172–183

    Article  Google Scholar 

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Funding

The authors would like to acknowledge the Kiran Division, Department of Science and Technology, Govt. of India, for funding this research work through the SR/WOSA/ET-153/2017 Research Grant. The authors also thank the anonymous reviewers for their encouraging reviews and recommendations.

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Authors and Affiliations

  1. ABV-IIITM, Gwalior, India

    R. Jenkin Suji, W.Wilfred Godfrey & Joydip Dhar

  2. RGPV, Bhopal, India

    Sarita Singh Bhadauria

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  1. R. Jenkin Suji
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  2. Sarita Singh Bhadauria
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  3. W.Wilfred Godfrey
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  4. Joydip Dhar
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Correspondence to R. Jenkin Suji.

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Suji, R.J., Bhadauria, S.S., Godfrey, W. et al. On using a Particle Image Velocimetry based approach for candidate nodule detection. Multimed Tools Appl 82, 22871–22888 (2023). https://doi.org/10.1007/s11042-023-14493-z

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  • DOI: https://doi.org/10.1007/s11042-023-14493-z

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