Microretina: An “Open Source” Tool for Retinal Images Analysis

Vincenzo Bonaiuto; Massimo Gabrieli; Fausto Sargeni

doi:10.4028/www.scientific.net/MSF.879.244

Paper Titles

Influence of Microalloying Elements Ti and Nb on Recrystallization during Annealing of Advanced High-Strength Steels
p.217

Regularities of Microstructure Evolution and Strengthening Mechanisms of Austenitic Stainless Steels Subjected to Large Strain Cold Working
p.224

Effect of Temperature and Strain Rate on the Mechanical Properties of 99.5 Aluminium Rods Extruded by KOBO
p.230

Miniaturized Flow-Through Bioreactor for Processing and Testing in Pharmacology
p.236

Microretina: An “Open Source” Tool for Retinal Images Analysis
p.244

Phase Stability Study of the Shape Memory Alloy CuAl-X (X: Be, Zn, Ti, Ni, Ag and Au) by Ab Initio Calculations
p.250

Phase Constitution and Martensitic Transformation Behavior of Au-51Ti-18Co Biomedical Shape Memory Alloy Heat-Treated at 1173K to 1373K
p.256

Thermal Plasticity Index of Nanostructured N-Based Coatings on HSS 6-5-2 (1.3343) Tool Steel
p.262

Microstructural Investigation of Oxynitrocarburized Components Processed at Different Temperatures
p.268

HomeMaterials Science ForumMaterials Science Forum Vol. 879Microretina: An “Open Source” Tool for Retinal...

Microretina: An “Open Source” Tool for Retinal Images Analysis

Abstract:

Several are the instrumental tests currently available in the diagnosis of retinal diseases. Their outputs are typically images of anatomical portions of the eye. However, these are useful to highlight only few aspects of the characteristic lesions of the occurring pathology. For this reason, the clinician needs to have tools able to perform comparative analysis of the different diagnostic images by using procedures of integration of the different clinical information contained in each of them. In this paper, we will describe a new medical software tool (MicroRetina) tailored for the comparative analysis of images of the fundus oculi, acquired during diagnostic tests that make use of different medical instruments. The developed software is an open system able to manage images acquired by different instruments and it will be able to give a helpful support to one of the existing problem in performing diagnosis by using images of the fundus oculi. In particular, it will allow the clinician to perform in real time the comparative analysis of the different clinical findings allowing him to have further diagnostic information not otherwise available by the analysis of the single images only.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Materials Science Forum (Volume 879)

Pages:

244-249

DOI:

https://doi.org/10.4028/www.scientific.net/MSF.879.244

Citation:

Cite this paper

Online since:

November 2016

Authors:

Vincenzo Bonaiuto*, Massimo Gabrieli, Fausto Sargeni

Keywords:

Diagnostics, Image Processing, Ophthalmology

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] D.P. Langston Manual of Ocular Diagnosis and Therapy, Lippincott Williams &Wilkins, (2002).

Google Scholar

[2] A. Chopdar: Fundus fluorescein angiography, (Oxford: Butterworth-Heinemann, 1996).

Google Scholar

[3] S.L. Owens Indocyanine green angiography, Ophthalmology Journal, English Ed., 1996; 80: 263-266.

Google Scholar

[4] A. Obana Y. Gohto M. Matsumoto T. Miki K. Nishiguti: Indocyanine green angiographic features prognostic of visual outcome in the natural course of patients with age related macular degeneration, (Ophthalmology Journal, English Ed., 1999; 83: 429-437).

DOI: 10.1136/bjo.83.4.429

Google Scholar

[5] N.R. Sabates: The MP-1 Microperimeter Clinical Applications in Retinal Pathologies, (Ophthalmology Journal, English Ed., 2005; 33: 4: 12-17).

Google Scholar

[6] J. Serra: Image Analysis and Mathematical Morphology, (Academic Press, 1982).

Google Scholar

[7] J.C. Russ: The Image Processing Handbook, (IEEE Press, 1999).

Google Scholar

[8] K.W. Pratt: Digital Image Processing, ( John Wiley & Sons, Inc., 1991).

Google Scholar

[9] Lumbroso B, Huang D, Jia Y, Fujimoto JA, Rispoli M (2014) Optical coherence tomography angiography: New clinical terminology Clinical guide to Angio-OCT, jJAYPEE, pp.5-7.

DOI: 10.5005/jp/books/12389_2

Google Scholar

[10] Roland Marduel (2015) Angio OCT, Dye Less Angiography, A New Approach of Age Related Macular Degeneration. Advances in Ophthalmology & Visual System Volume 2 Issue 2 – (2015).

DOI: 10.15406/aovs.2015.02.00034

Google Scholar

[11] Sangyeol Lee; Abramoff, M.D.; Reinhardt, J. M: Validation of Retinal Image Registration Algorithms by a Projective Imaging Distortion Model, Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE 6471 – 6474.

DOI: 10.1109/iembs.2007.4353841

Google Scholar