1Digital Communications Signal Processing (DCSP) Research Lab., Faculty of Electrical and Computer Engineering, Shahid Rajaee Teacher Training University (SRTTU), Tehran, Iran.
2Department of Biomedical Engineering, College of Engineering and School of Medicine, Wayne State University, Detroit, MI 48201, USA
3Brain& Intelligent Systems Research Lab., Department of Electrical and Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
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.