International Journal of Computer Applications (0975 – 8887) Volume 65– No.12, March 2013 A New Nonlinear Anisotropic – Wiener Method for Speckle Noise Reduction in Optical Coherence Tomography Ahmed H. Samak Ass. Professor of computer science, Department of Mathematics, Faculty of Science, Menofia University, Egypt ABSTRACT Removing noise from the original medical image is still a challenging research in image processing. This paper presents a new method for speckle noise reduction in Optical coherence Tomography (OCT). Stationary wavelet transform (SWT) is employed to provide effective representation of the noisy coefficients. Nonlinear Anisotropic filtering of the Details coefficients improves the denoising efficiency and effectively preserves the edge features while wiener filter improves to denoising approximate coefficients. The performance of the proposed method is compared with Nonlinear Anisotropic filter, Wiener filter, Lee filter and Frost filter and analyzed based on the peak signal-to-noise ratio (PSNR). Keywords: image denoising; wavelet transform; Optical coherence Tomography; Nonlinear Anisotropic filter. 1. INTRODUCTION Medical images are usually corrupted by noise in its acquisition and Transmission. The main objective of Image denoising techniques is necessary to remove such noises while retaining as much as possible the important signal features. Over the past few decades, technological advancements in optical devices and laser technology have given birth to a novel non-invasive optical biomedical imaging technique called optical coherence tomography (OCT). OCT is an imaging technology that allows for in vivo, non- invasive high-resolution, two- or three dimensional cross- sectional imaging of the morphology of partially transparent and highly scattering biological tissues on a microscopic level [1]. The axial OCT resolution can range from 0:5 mm to few micrometers, while the penetration depth in biological tissue is limited to 1:5 to 2mm. Hence, OCT is uniquely suited for performing non-invasive optical biopsy of biological tissues such as skin, cornea, retina, arterial plaques, cervical and gastro-intestinal epithelium, etc. The system diagram presented in figure1 representing the noise model in OCT imaging system. S(x; y) is the noise free OCT image, f(x; y) is the noise observation of s(x; y), m(x; y) and n(x; y) are the speckle and additive noise [2]. Figure1: Noise model in OCT imaging system. In the past, extensive research has been conducted both in the fields of medical imaging and remote sensing for suppressing speckle noise. Many methods have been developed to improve the image quality degraded by speckle noise. Several speckle- reduction procedures are described by [3]. In this work, speckle reduction techniques in OCT are classified into four categories: polarization diversity, spatial compounding, frequency compounding, and digital signal processing. These categories can be summed into either numerical image processing algorithms, or alternative detection schemes of the OCT system design. Polarization diversity, spatial compounding, and frequency compounding are based on modification to the OCT system design. A wavelet based soft thresholding technique has been previously applied to OCT images corrupted by speckle noise [4]. It computes the undecimated wavelet transform and applies soft thresholding to the horizontal, vertical, and diagonal sub bands. The threshold is obtained using the statistics of the wavelet coefficients. The wavelet based technique described in [4] does not reduce the image sharpness significantly but the execution time for the algorithm is about 7 min using Matlab implementation. Modified Lee and Kuan adaptive filters have been applied to SAR speckle reduction [5]. Anisotropic diffusion is another digital algorithm that has been previously applied for speckle noise reduction in OCT images. For example, in references [6, 7] the gradient of the image is used for the calculation of the diffusion coefficient with no consideration to the actual noise present. Bo Chong and Yong-Kai Zhu proposed a novel speckle noise reduction algorithm in OCT. The algorithm is based on block- matching 3D filter modified by morlet wavelet decomposition. Original OCT image data transformed by logarithmic compression is decomposed into 10 components by morlet wavelet for three levels. Each component is proposed by a suited BM3D filter and the output image is reconstructed by wavelet reverse transformation [8].Mashaly, A.S. and et al presented an adaptive mathematical morphological filter is proposed to reduce the speckle noise in SAR images [9].