CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Lung Nodule Retrieval System

by Gagan Deep, Lakhwinder Kaur, Savita Gupta
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 64 - Number 16
Year of Publication: 2013
Authors: Gagan Deep, Lakhwinder Kaur, Savita Gupta
10.5120/10717-4641

Gagan Deep, Lakhwinder Kaur, Savita Gupta . Lung Nodule Retrieval System. International Journal of Computer Applications. 64, 16 ( February 2013), 13-18. DOI=10.5120/10717-4641

@article{ 10.5120/10717-4641,
author = { Gagan Deep, Lakhwinder Kaur, Savita Gupta },
title = { Lung Nodule Retrieval System },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 64 },
number = { 16 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 13-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume64/number16/10717-4641/ },
doi = { 10.5120/10717-4641 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:16:36.588708+05:30
%A Gagan Deep
%A Lakhwinder Kaur
%A Savita Gupta
%T Lung Nodule Retrieval System
%J International Journal of Computer Applications
%@ 0975-8887
%V 64
%N 16
%P 13-18
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Early detection and removal of pulmonary nodules significantly improves long term survival rates for patients with lung cancer. This paper provides the overview of different methods used in the retrieval system of lung nodules by a comprehensive review of existing literature. Firstly, the high level features of DICOM CT images are used for retrieval of filtered lung images from the database. The preprocessing step is used for separation of lungs fields on the filtered images. Linear Binary Pattern extracts the low level features from extracted lung areas to perform the segmentation. The technique of template matching further uses to retrieve the abnormal nodules from Lung data set.

References
  1. American Cancer Society. Factbox: Latest U.S. cancer statistics (2011). American Cancer Society, Atlanta, GA. http://www.reuters.com/article/2011/06/17/us-factbox-cancer-idUSTRE75G0PL20110617
  2. United States National Institute of Health www.nih.gov
  3. Muller H, Michoux N, Bandon D, Geissbuhler A, (2004) “A review of content-based image retrieval systems in medical applications-clinical benefits and future directions”, Int J Med Inform, Vol.73(1), pp.1-23.
  4. Digital Imaging and Communications in Medicine (DICOM) - Part 1: Introduction and Overview, DICOM PS 3.1, National Electrical Manufacturers Association - NEMA. Rosslyn, Virginia, USA. 2004.
  5. Bas Revet, (1997) DICOM Cook Book for Implementations in Modalities, Nederland: Philips Medical Systems.
  6. Antonio da Luz Jr., Daniel D. Abdala, Aldo v. Wangenheim, Eros Comunello, (2006) “Analyzing DICOM and non-DICOM Features in Content-Based Medical Image Retrieval: A Multi-Layer Approach”, Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06), pp. 93-98.
  7. A. Grace Selvarani, S. Annadurai, (2007) “Medical image retrieval by combining low level features and DICOM features”, IEEE International Conference on Computational Intelligent and Multimedia Applications, Vol. 1, pp. 587 – 589.
  8. Sheng Wu, Yi Xue, Gong Cheng, Xue Za Zhi, (2009) “Medical image retrieval by high level semantic features and low level content features of image”, PubMed - indexed for MEDLINE, Vol.26(6), pp.1237-40.
  9. Hong Shao, Wen-cheng Cui, Hong Zhao, (2004) “Medical image retrieval based on visual contents and text information”, IEEE International Conference on Systems, Man and Cybernetics, Vol.1, pp.1098-1103.
  10. M. Prokop I. Sluimer, A. Schilham, B. van Ginneken, (2006) “Computer analysis of computed tomography scans of the lung: A survey”, IEEE Transactions on Medical Imaging, Vol.25(4), pp.385-405.
  11. Pradeep Singh, Sukhwinder Singh, Gurjinder Kaur, (2008) “A Study of Gaps in CBMIR using Different Methods and Prospective”, Proceedings of world academy of science, engineering and technology, Vol. 36, ISSN 2070-3740, pp. 492-496.
  12. Zhen Ma, João Manuel R. S. Tavares, R. M. Natal Jorge, (2009) “A review on the current segmentation algorithms for medical images”, 1st International Conference on Imaging Theory and Applications (IMAGAPP), Lisboa, Portugal, INSTICC Press, pp. 135-140.
  13. Ojala, T., Pietikäinen, M., Mäenpää, T., (2000) "Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns", Lecture Notes in Computer Science, Vol. 1842, pp. 404-420.
  14. Alexander S. Behnaz, James Snider, Chibuzor Eneh et.al, (2010) “Quantitative CT for Volumetric Analysis of Medical Images: Initial Results for Liver Tumors”, Medical Imaging 2010, Proc. of SPIE, Vol 7623-76233U.
  15. Vincent Chu and Ghassan Hamarneh, (2006) “MATLAB-ITK Interface for Medical Image Filtering, Segmentation and Registration”, Medical Imaging 2006: Image Processing, Proc. of SPIE, Vol. 6144, 61443T1-8.
  16. Ojala, T., Pietikäinen, M., Mäenpää, T., (2002) “Multiresolution gray-scale and rotation invariant texture classification with Local Binary Patterns”, IEEE Transaction, Vol. 24, pp.971–987.
  17. Edward H.S. Lo, Mark R. Pickering, Michael R. Frater, John F. Arnold, (2011) “Image segmentation from scale and rotation invariant texture features from the double dyadic dual-tree complex wavelet transform”, Image and Vision Computing (ELSEVIER), Vol. 29 (1), pp. 15-28.
  18. Pietikainen, M., Ojala, T., Xu, Z., (2000) “Rotation-Invariant texture classification using feature distributions”, Pattern Recognition, Vol. 33, pp. 975-985.
  19. Zhenhua Guo, Lei Zhang, David Zhang, (2010) “Rotation invariant texture classification using LBP variance (LBPV) with global matching”, Biometrics Research Centre, Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China, Pattern Recognition, Vol. 43, pp.706–719.
  20. Timo Ojala, Matti Pietikanen, (1999) “Unsupervised texture segmentation using feature distributions”, Machine Vision and Media Processing Group, Pattern Recognition, Vol. 32, pp.477-486.
  21. Caifeng Shan, Shaogang Gong, Peter W. McOwan, (2009) “Facial expression recognition based on Local Binary Patterns: A comprehensive study”, Image and Vision Computing, Vol. 27, pp. 803–816.
  22. Fredriksson, K., (2001) “Rotation Invariant Template Matching”, TECHREPORT, PhD Thesis, A-2001-3, pp.139.
  23. Alsaade, F., (2012) “Fast and Accurate Template Matching Algorithm Based on Image Pyramid and Sum of Absolute Difference Similarity Measure”, Research Journal of Information Technology, Vol. 4, pp. 204-211.
  24. Heuberger, J., Geissbuhler, A., Muller, H., (2005) “Lung CT segmentation for image retrieval using the Insight Toolkit (ITK)”, Medical Imaging and Telemedicine, (MIT2005). Hopitaux University of Geneva, China.
  25. Ojala, T., PietikaÈinen, M., Harwood, D., (1996) “A Comparative Study of Texture Measures with Classification Based on Feature Distributions”, Pattern Recognition. Vol. 29, pp. 51-59.
  26. Haralick, R.M., (1979) “Statistical and structural approaches to Texture”, Proc IEEE 67. Vol.5, pp.786-804.
  27. Lam, W.-K., Li, C.-K., (1997) “Rotated Texture Classification by Improved Iterative Morphological Decomposition”, IEE Proc. - Vision, Image and Signal Processing. Vol.144, pp.171-179.
  28. Madiraju, S.V.R., Liu, C.C., (1995) “Rotation Invariant Texture Classification Using Covariance”, Proc. Int. Conf. Image Processing. pp. 262-265.
  29. Mao, J., Jain, A.K., (1992) “Texture Classification and Segmentation Using Multiresolution Simultaneous Autoregressive Models”, Vol. 25, pp.173-188.
  30. Wu, Y., and Yoshida, Y., (1995) “An Efficient Method for Rotation and Scaling Invariant Texture Classification”, Proc. IEEE Int'l Conf Acoustics, Speech, and Signal Processing, Vol.4, pp. 2519-2522.
  31. Wu, Y., and Yoshida, Y., (1995) “An Efficient Method for Rotation and Scaling Invariant Texture Classification”, Proc. IEEE Int'l Conf Acoustics, Speech, and Signal Processing, Vol.4, pp. 2519-2522.
  32. Siemens Corporation, “SIENET Sky – DICOM CD Viewer” http://www.webbuyersguide.com/Product/viewproduct.aspx?id=42372
  33. Maciej Frankiewicz, “http://www.radiantviewer.com/”, Poland.
  34. Kascic, E., NBIA - National Cancer Imaging Archive NCIA (version 4.0): The NCI's repository for DICOM-based images (https://cabig.nci.nih.gov/tools/NCIA)
  35. H. Y. Kim and S. A. Araújo, (2007) “Grayscale Template-Matching Invariant to Rotation, Scale, Translation, Brightness and Contrast”, IEEE Pacific-Rim Symposium on Image and Video Technology, Lecture Notes in Computer Science, Vol.4872, pp.100-113.
  36. Ashley Aberneithy, (2007) “Automatic Detection of Calcified Nodules of Patients with Tuberculous”, University College, London.
Index Terms

Computer Science
Information Sciences

Keywords

DICOM CT scans Lung Nodules High Level Features Low Level Features LBP Template Matching

Powered by PhDFocusTM