Preprocessing of 2-Dimensional Gel Electrophoresis Images Applied to Proteomic Analysis: A Review

Various methods and specialized software programs are available for processing twodimensional 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?ltering techniques applied to synthetic gel images,through analyzing the performance of the?lters under speci?c 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(d B).We compared the performanceof wavelet,contourlet,total variation(TV),and wavelet-total variation(WTTV)techniques using parameters SNR and spot ef?ciency.In terms of spot ef?ciency,contourlet and TV were more sensitive to noise than wavelet and WTTV.Wavelet worked the best for images with SNR ranging 10–20 d B,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 d B)of Rayleigh and exponential noise in terms of SNR.Finally,the performance of the non-linear?ltering techniques was evaluated using a real 2-DGE image with previously identi?ed proteins marked.Wavelet achieved the best detection rate for the real image.