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 h...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 hor- izontal streaking, fuzzy spots, and background noise, which greatly complicate computational anal- ysis. 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 exponen- tial, with signal-to-noise ratios （SNRs） ranging 8-20 decibels （dB）. We compared the performanceof 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 sen- sitive 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 per- formance 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.展开更多
基金supported by the Instituto Tecnológico Metropolitano (ITM) of Medellín, Colombia (Grant No. P14227) awarded to SR
文摘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 hor- izontal streaking, fuzzy spots, and background noise, which greatly complicate computational anal- ysis. 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 exponen- tial, with signal-to-noise ratios （SNRs） ranging 8-20 decibels （dB）. We compared the performanceof 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 sen- sitive 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 per- formance 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.
