International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Lung Region Segmentation using Image Data Analysis

Abstract

It is very common in medical imaging that the anatomy of interest occupies only a very small part of the image. Therefore, most of the removed spots are in the background area, while these small organs (anomalies) are of greater importance. One of the most difficult tasks in analyzing medical images is the segmentation of medical images, which identifies pixels or organ damage from medical background images such as CT or MRI images. The main goal of the lung region extraction process is to capture the lung region and determine the regions of interest (ROI) on the CT image.

Country : India

1 Ms.S.Pradeeba2 Dr.S.Kumar Ganesh

  1. M.E. Scholar, Dept of Communication Systems, Paavai Engineering College, Namakkal, Tamilnadu, India
  2. Associate Professor, Dept of ECE, Paavai Engineering College, Namakkal, Tamilnadu, India

IRJIET, Volume 4, Issue 5, May 2020 pp. 44-50

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References

  1. P. B. Sangamithraa and S. Govindaraju, "Lung tumour detection and classification using EK-Mean clustering," 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, 2016, pp. 2201-2206.
  2. S. Kalaivani, P. Chatterjee, S. Juyal and R. Gupta, "Lung cancer detection using digital image processing and artificial neural networks," 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, 2017, pp. 100-103.
  3. S. Hossain, S. Najeeb, A. Shahriyar, Z. R. Abdullah and M. ArifulHaque, "A Pipeline for Lung Tumor Detection and Segmentation from CT Scans Using Dilated Convolutional Neural Networks," ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, United Kingdom, 2019, pp. 1348-1352.
  4. P. Rao, N. A. Pereira and R. Srinivasan, "Convolutional neural networks for lung cancer screening in computed tomography (CT) scans," 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noida, 2016, pp. 489-493.
  5. Shakeel, P.,  Tobely, Tarek,  Al-Feel, Haytham,  Manogaran, Gunasekaran& .S, Baskar, “Neural Network Based Brain Tumor Detection Using Wireless Infrared Imaging Sensor”, IEEE Access. PP. 1-1. 10.1109/ACCESS.2018.2883957, 2017.
  6. A.Kumar, M. Fulham, D. Feng and J. Kim, "Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancer," in IEEE Transactions on Medical Imaging, pp.1-1, 2019.
  7. G. Wang et al., "Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning," in IEEE Transactions on Medical Imaging, vol. 37, no. 7, pp. 1562-1573, July 2018.
  8. Alakwaa W, Nassef M, Badr A: Lung cancer detection and classification with 3D convolutional neural network (3D-CNN).  Lung Cancer 8(8):409, 2017.
  9. Anirudh R, Thiagarajan JJ, Bremer T, Kim H, “Lung nodule detection using 3D convolutional neural network trained on weakly labeled data”.  In: Medical imaging 2016: Computer-Aided Diagnosis, vol 9785, 2016, p 978532. 
  10. Armato SG I, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, et al, “The lung image database consortium (LIDC) and image database resources initiative (IDRI): a completed reference database of lung nodules on CT scans”, Med phys 38(2):915-931, 2011.
  11. Bar Y, Diamant I, Wolf L, Greenspan H: Deep learning with non medical training used for chest pathology identification.  In: Medical imaging 2015: Computer-Aided Diagonsis, vol 9414, 2015, p 94140v.
  12. Baumgartner CF, Koch LM, Pollefeys M, Konukoglu E: “An exploration of 2D and 3D deep learning techniques for cardiacmr image segmentation”.  In: International Workshop on Statistical Atlases and Computational Models of the Heart.  Springer, 2017, pp111-119.
  13. Bergamo A, Torresani L, Fitzgibbon AW: pincodes: “Learning a compact code for novel-category recognition”.  In: Advance in Neural Information Processing System, 2011, pp 2088-2096.
  14. Cai J, Lu L, Xie Y, Xing F, Yang L (2017) “improving deep pancreas segmentation in CT and MRI images via recurrent neural contextual learning and direct loss function”, arXiv:1707.04912.
  15. Chen H, Dou Q, YuL, Qin J, Heng PA: “Voxresnet: deep voxelwise residual networks for brain segmentation from 3D MR images”.  Neuroimage, 170:446-455, 2017.
  16. Chen H, Ni D, Qin J, Li S, Yang X, Wang T, Heng PA: “Standard plane localization in fetal ultrasound via domain transferred deep neural networks”, IEEE J Biomed Health Inform, 19(5):1627-1636, 2015.
  17. Chen H, Qi X, Cheng PA et al: “Deep contextual networks for neuronal structure segmentation”, In: AAAI, 2016, pp 1167-1173.
  18. Chen H, Qi X, Yu L, Heng PA: “DCAN: deep contour-aware networks for accurate gland segmentation”, In: Proceeding of the IEEE conference on computer Vision and Pattern Recognition, 2016, pp2487-2496.
  19. Chen J, Yang L, Zhang Y, Alber M, Chen DZ, “Combining fully convolutional and recurrent neural networks for 3D biomedical image segmentation” In: Advances in Neural Information Processing System, 2016, pp3036-3044.
  20. Cheng D, Liu M, “Combining convolutional and recurrent neural networks for Alzheimer’s disease diagnosis using pet images”,  In:2017 IEEE International Conference on imaging systems And Techniques (IST). IEEE, 2017, pp 1-5.

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