The Application of Multi-Layer Artificial Neural Networks in Speckle Reduction (Methodology)

Pishgoo, M.R.; N. Avanaki, M.R.; Ebrahimpour, R.

doi:10.22061/jecei.2014.45

Document Type : Original Research Paper

Authors

¹ Digital Communications Signal Processing (DCSP) Research Lab., Faculty of Electrical and Computer Engineering, Shahid Rajaee Teacher Training University (SRTTU), Tehran, Iran.

² Department of Biomedical Engineering, College of Engineering and School of Medicine, Wayne State University, Detroit, MI 48201, USA

³ Brain& Intelligent Systems Research Lab., Department of Electrical and Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

https://doi.org/10.22061/jecei.2014.45

Abstract

Optical Coherence Tomography (OCT) uses the spatial and temporal coherence properties of optical waves backscattered from a tissue sample to form an image. An inherent characteristic of coherent imaging is the presence of speckle noise. In this study we use a new ensemble framework which is a combination of several Multi-Layer Perceptron (MLP) neural networks to denoise OCT images. The noise is modeled using Rayleigh distribution with the noise parameter, sigma, estimated by the ensemble framework. The input to the framework is a set of intensity and wavelet statistical features computed from the input image, and the output is the estimated sigma value for the noise model. In this article the methodology of this technique is explained.

Keywords

20.1001.1.23223952.2014.2.1.6.3

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Journal of Electrical and Computer Engineering Innovations (JECEI)

The Application of Multi-Layer Artificial Neural Networks in Speckle Reduction (Methodology)

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Volume 2, Issue 1 - Serial Number 3
January 2014
Pages 37-42

The Application of Multi-Layer Artificial Neural Networks in Speckle Reduction (Methodology)

References

References

Send comment about this article

Volume 2, Issue 1 - Serial Number 3January 2014Pages 37-42

Volume 2, Issue 1 - Serial Number 3
January 2014
Pages 37-42