Automated identification of SD-optical coherence tomography derived macular diseases by combining 3D-block-matching and deep learning techniques

ABSTRACT

This paper reveals an automatic approach for macular diagnostic by the use of Optical Coherence Tomography (OCT) in the evaluation process. At preliminary behaviour levels, the evaluation of macular zone in OCT scan is characterised by an error prone task, related to the experience and the attention of ophthalmologists. Thus, different techniques of OCT-image analysis help the option of obtaining a consistent and independent diagnosis to identify macular degeneration behaviour. In this work, we report an automated approach based on a combined filtering and classification strategy. The presented method is validated on a real integrated diabetic oedema macular (DME) and age related to macular degeneration (AMD). The experimental results illustrate the high accuracy of the results of the proposed method compared to the ground truth. Furthermore, a comparative study with existing techniques is presented in order to demonstrate the efficiency and the superiority of the proposed technique.

KEYWORDS:

• Optical coherence tomography
• OCT image filtering
• block-matching and 3D filtering (BM3D)
• recurrent neural networks and deep belief networks

Acknowledgments

We would like to express our gratitude to the editor and anonymous reviewers for their constructive comments that will lead to this manuscript’s improvement in quality and representation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors received no specific funding for this study.

Notes on contributors

Ilhem Mezni

Ilhem Mezni is a PhD Student in Biophysics and medical imaging from the University of Tunis El Manar, ISTMT. She is currently an Associate Professor at the Faculty of medicine of Tunis and member of the research group in Laboratory of Biophysics and medical technologies at the same university. Her research interests include biophysics, pattern recognition, Optical coherence tomography and Monte Carlo simulation.

Amine Ben Slama

Amine Ben Slamais a PhD Doctor in Biophysics and medical imaging from the University of Tunis El Manar, ISTMT. He is a member of research group in Laboratory of Biophysics and medical technologies at the same university. His research interests include signal and image processing, and deep learning methods.

Zouhair Mbarki

Zouhair Mbarki was born in Tunisia, he received the master degree in Automatic from the Higher School of Sciences and Techniques of Tunis, in 2011 and the thesis degree in image processing in 2017. His research interests are focused on image processing, smoothing, restoration and segmentation.

Hassen Seddik

Hassen Seddik was born in 15 October 1970 in Tunisia, he has obtained the electromechanical engineer degree in 1995 and followed by the master degree in “signal processing: speaker recognition” and the thesis degree in image processing “watermarking using non-conventional transformations”. He has over 14 international journals papers and 65 conference papers. His domain of interest is: Audio-image and video processing applied in filtering, encryption and watermarking. He belongs to the CEREP research unit and supervises actually five thesis and 08 masters in the field.

Hedi Trabelsi

Hedi Trabelsiborn in 1975 in Tunis (Tunisia), he received the B.Sc. degree in Biophysics from the faculty of Sciences of Tunis, He is currently Professor at the higher institute of medical technologies of Tunisia University of Tunis El Manar, member of the Laboratory of biophysics and medical technologies. His research interests are focused on diffuse optical Tomography and physics simulation.