Formation and Fusion of Projection Images from 11 Layers of Retina Using Statistical Indicators to Obtain an Image with Appropriate Contrast from the Retinal Depth

Document Type : Original Article (s)

Authors

1 MSC Student, Department of Biomedical Engineering, School of Medicine AND Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran

2 Associate Professor, Department of Biomedical Engineering, School of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran

3 Associate Professor, Department of Ophthalmology , School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Abstract

Background: Optical coherence tomography (OCT) is a progressively important modality for the noninvasive management of retinal diseases, including age-related macular degeneration (AMD), glaucoma, and diabetic macular edema. Spectral domain OCT (SD-OCT) generates 3-dimensional (3-D) volumes, which have proven to be useful in clinical practice. In this regards, forming projection images limited to each layer of retina not only represents information of each layer but also, it would individually display the amount of vulnerability in the each layer caused by a specific disease.Methods: In the first step, a projection image associated with 11 retinal layers was formed with merging the levels and voxels between each pair of boundaries by using different statistical indicators including average, mean, maximum and minimum. Then, retinal layers with more information through statistical indicators were fused with each other to gain an image without any noise and other deficiencies to possess a better clarify and contrast of specific information in X-Y axis.Findings: Using different statistical methods such as average, mean, maximum, minimum and variance, projection images associated with each layer of retina were gotten. Each of these methods made better images in the specific layers than other methods used in the next steps. Fusing of layers with each other was also provided appropriate information from retinal depth in different parts. Contrast enhancement of images related with layers of fused images and creating more complete coordinate of vessels presence were important characteristics of final image. Conclusion: Resulting projection image would be used more effectively in the extraction of important characteristics of retina including vessels extraction as well as determination of confinement and the center of macular region. Moreover, newer multi-resolution transforms such as curvelet transform would be used to obtain better final fused image.

Keywords

References

  1. Fujimoto JG. Optical coherence tomography. Comptes Rendus de l'Academie des Sciences-Series IV-Physics 2001; 2(8): 1099-111.
  2. Hood DC, Fortune B, Arthur SN, Xing D, Salant JA, Ritch R, et al. Blood vessel contributions to retinal nerve fiber layer thickness profiles measured with optical coherence tomography. J Glaucoma 2008; 17(7): 519-28.
  3. Joshi GD, Sivaswamy J, Krishnadas SR. Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment. IEEE Trans Med Imaging 2011; 30(6): 1192-205.
  4. Garvin MK, Abramoff MD, Wu X, Russell SR, Burns TL, Sonka M. Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images. IEEE Trans Med Imaging 2009; 28(9): 1436-47.
  5. Lee K, Abràmoff MD, Niemeijer M, Garvin MK, Sonka M. 3-D segmentation of retinal blood vessels in spectral-domain OCT volumes of the optic nerve head. In: Molthen RC, Weaver JB, editors. Medical Imaging 2010: Biomedical Applications in Molecular, Structural, and Functional Imaging. Bellingham, WA: Spie-int Soc Optical Engineering, 2010.
  6. Lee K, Niemeijer M, Garvin MK, Kwon YH, Sonka M, Abramoff MD. Segmentation of the optic disc in 3-D OCT scans of the optic nerve head. IEEE Trans Med Imaging 2010; 29(1): 159-68.
  7. Qu G, Zhang D, Yan P. Information measure for performance of image fusion. Electronics Letters 2002; 38(7): 313-5.
  8. Burt PJ, Kolczynski RJ. Enhanced image capture through fusion. Proceedings of 4th International Conference; 1993 May 11-14; Berlin, Germany.
  9. Kafieh R, Rabbani H, Hajizadeh F, Ommani M. An accurate multimodal 3-D vessel segmentation method based on brightness variations on OCT layers and curvelet domain fundus image analysis. IEEE Trans Biomed Eng 2013; 60(10): 2815-23.
  10. Tsai VJD. Evaluation of multiresolution image fusion algorithms. Proceedings of Geoscience and Remote Sensing Symposium; 2004 Sep 20-24; Anchorage, Alaska.
  11. Daneshvar S, Ghassemian H. MRI and PET image fusion by combining IHS and retina-inspired models. Information Fusion 2010; 11(2): 114-23.
  12. Constantinos SP, Pattichis MS, Micheli-Tzanakou E. Medical imaging fusion applications: An overview. Proceedings of the 35th Asilomar Conference on Signals, Systems and Computers; 2001 Nov 4-7; Pacific Grove, CA, USA.
  13. Jalili J, Rabbani H, Akhlaghi M, Kafieh R, Mehridehnavi A. Forming projection images from each layer of retina using diffusion may based OCT segmentation. Proceedings of the 11th International Conference on Information Science, Signal Processing and their Applications; 2012 July 2-5; Montreal, QC.
  14. Choi M, Kim RY, Kim MG. The curvelet transform for image fusion. ISPRS 2004; 35: 59-64.
  15. Amolins K, Zhang Y, Dare P. Wavelet based image fusion techniques--An introduction, review and comparison. ISPRS Journal of Photogrammetry and Remote Sensing 2007; 62(4): 249-63.
Journal of Isfahan Medical School
Volume 31, Issue 255 - Serial Number 255
September and October 2013
Pages 1598-1607

Files

History

  • Receive Date: 13 February 2013
  • Revise Date: 01 November 2024
  • Accept Date: 06 April 2022

Share

How to cite

Statistics