Speckle noise reduction algorithm for optical coherence tomography based on interval type II fuzzy set
Speckle noise reduction algorithm for optical coherence tomography based on interval type II fuzzy set
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DOI：
10.1364/oe.15.015747
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发表时间：
2007-11-26
期刊：
影响因子：
3.8
通讯作者：
Bizheva, Kostadinka
中科院分区：
文献类型：
--
作者：
Puvanathasan, Prabakar;Bizheva, Kostadinka
针对光学相干层析成像中相干斑噪声的抑制问题，提出了一种基于区间II型模糊系统的小波系数软阈值相干斑抑制技术。该算法是最近发表的利用I型模糊系统滤除加性高斯噪声的方法的扩展。与第一类不同，区间第二类模糊阈值滤波器考虑了阈值计算中的不确定性，并根据这种不确定性调整小波系数。单个参数控制信噪比(SNR)的提高。将该算法应用于活体采集的人指尖光学相干层析图像，在边缘模糊不大的情况下，斑点噪声得到了抑制，图像信噪比提高了约10d B。与应用于同一图像的自适应Wiener和自适应Lee滤波进行了比较，证明了模糊II类算法在图像度量改善方面的优越性能。(C)2007年美国光学学会。
A novel speckle reduction technique based on soft thresholding of wavelet coefficients using interval type II fuzzy system was developed for reducing speckle noise in Optical Coherence Tomography images. The proposed algorithm is an extension of a recently published method for filtering additive Gaussian noise by use of type I fuzzy system. Unlike type I, interval type II fuzzy based thresholding filter considers the uncertainty in the calculated threshold and the wavelet coefficient is adjusted based on this uncertainty. A single parameter controls the signal-to-noise (SNR) improvement. Application of this novel algorithm to optical coherence tomograms acquired in-vivo from a human finger tip show reduction in the speckle noise with little edge blurring and image SNR improvement of about 10dB. Comparison with adaptive Wiener and adaptive Lee filters, applied to the same image, demonstrated the superior performance of the fuzzy type II algorithm in terms of image metrics improvement. (c) 2007 Optical Society of America.