Various methods and specialized software programs are available for processing two-dimensional gel electrophoresis (2-DGE) images. However, due to the anomalies present in these images, a reliable, automated, and highly reproducible system for 2-DGE image analysis has still not been achieved. The most common anomalies found in 2-DGE images include vertical and horizontal streaking, fuzzy spots, and background noise, which greatly complicate computational analysis. In this paper, we review the preprocessing techniques applied to 2-DGE images for noise reduction, intensity normalization, and background correction. We also present a quantitative comparison of non-linear filtering techniques applied to synthetic gel images, through analyzing the performance of the filters under specific conditions. Synthetic proteins were modeled into a two-dimensional Gaussian distribution with adjustable parameters for changing the size, intensity, and degradation. Three types of noise were added to the images: Gaussian, Rayleigh, and exponential, with signal-to-noise ratios (SNRs) ranging 8–20 decibels (dB). We compared the performance of wavelet, contourlet, total variation (TV), and wavelet-total variation (WTTV) techniques using parameters SNR and spot efficiency. In terms of spot efficiency, contourlet and TV were more sensitive to noise than wavelet and WTTV. Wavelet worked the best for images with SNR ranging 10–20?dB, whereas WTTV performed better with high noise levels. Wavelet also presented the best performance with any level of Gaussian noise and low levels (20–14?dB) of Rayleigh and exponential noise in terms of SNR. Finally, the performance of the non-linear filtering techniques was evaluated using a real 2-DGE image with previously identified proteins marked. Wavelet achieved the best detection rate for the real image.
蛋白质组学是分析在生物体内产生的一些列蛋白质，包括分析蛋白表达、蛋白存在与否及其丰度的直接测量等，由此来整体而全面理解细胞进程和识别药物靶点、诊断和预后标志物。在比较蛋白质组学研究中应用最广的技术就是二维凝胶电泳(2-DGE)图像技术。目前已经有各种各样的方法和专门的软件程序来处理2-DGE图像，如MELANIE和PDQuest等。然而,由于2-DGE图像通常存在一些异常现象,一个可靠的、自动化、和高度可再现的2-DGE图像分析系统仍未完成。在2-DGE图像最常见的异常现象包括垂直和水平图像拖尾、模糊点以及背景噪音,这些异常大大增加了计算分析的复杂性。在本文中,我们回顾了2-DGE图像的预处理技术，主要包括2-DGE图像的降噪、密度标准化和背景校正。通过分析在特定条件下滤波器的性能，我们定量地比较了应用于合成凝胶图像的非线性滤波技术。合成蛋白质被建模成一个二维高斯分布，并且可通过调整参数改变蛋白图像的大小、密度和退化。高斯、瑞利和指数这三种类型的噪声被添加到图片，其信噪比范围在8-20分贝(dB)。我们通过使用参数信噪比和点效率来比较了小波、轮廓波、总变异以及小波总变异技术的性能。就点效率而言,轮廓波和总变异比小波和小波总变异对噪声更敏感。小波能最好地处理信噪比范围在10 - 20分贝的图像,而小波总变异则能更好地处理高噪音水平的图像。就信噪比而言，小波也呈现出最佳性能来处理任何程度的高斯噪声、低水平(20-14 dB)的瑞利噪声和指数噪声。最后, 通过使用已经确定蛋白质标记的真实2-DGE图片来评估非线性滤波技术的性能，我们发现小波技术能够取得真实图片的最好检出率。





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