Authors:
Saba Adabi
1
;
Silvia Conforto
1
;
Anne Clayton
2
;
Adrian G. Podoleanu
3
;
Ali Hojjat
3
and
Mohammad R. N. Avanaki
2
Affiliations:
1
Roma Tre University, Italy
;
2
Wayne State Univeristy, United States
;
3
University of Kent, United Kingdom
Keyword(s):
Optical Coherence Tomography, Multi-Layer Perceptron (MLP), Speckle Noise Reduction, Artificial Neural Network (ANN).
Related
Ontology
Subjects/Areas/Topics:
Biomedical Optics
;
Computational Optical Sensing and Imaging
;
Optical Instrumentation
;
Optics
;
Photonics, Optics and Laser Technology
;
Spectroscopy, Imaging and Metrology
Abstract:
Optical Coherence Tomography (OCT) offers three dimensional images of tissue microstructures. Although OCT imaging offers a promising high resolution method, due to the low coherent light source used in the configuration of OCT, OCT images suffers from an artefact called, speckle. Speckle deteriorates the image quality and effects image analysis algorithm such as segmentation and pattern recognition. We present a novel and intelligent speckle reduction algorithm to reduce speckle based on an ensemble framework of Multi-Layer Perceptron (MLP) neural networks. We tested the algorithm on images of retina obtained from a spectrometer-based Fourier-domain OCT system operating at 890 nm, and observed considerable improvement in the signal-to-noise ratio and contrast of the images.