Joint pre-processing framework for two-dimensional gel electrophoresis images based on nonlinear filtering, background correction and normalization techniques

BMC Bioinformatics

卷:21

Edilson Delgado-Trejos¹  Sarah Rothlisberger²  Manuel Mauricio Goez³  Maria C. Torres-Madronero³ 

[1] AMYSOD Lab (Parque i), CM&P Research Group, Quality and Production Department, Instituto Tecnologico Metropolitano ITM;

[2] Biomedical Innovation and Research Group, Faculty of Applied and Exact Sciences, Instituto Tecnologico Metropolitano ITM;

[3] Smart Machine and Pattern Recognition Laboratory - MIRP, Faculty of Engineering, Instituto Tecnologico Metropolitano ITM;

关键词: Adaptive histogram equalization;    Multilevel thresholding;    Nonlinear diffusion;    Pre-processing;    Two-dimensional gel electrophoresis;   

DOI  :  10.1186/s12859-020-03713-0

来源: DOAJ

【 摘 要 】

Abstract Background Two-dimensional gel electrophoresis (2-DGE) is a commonly used tool for proteomic analysis. This gel-based technique separates proteins in a sample according to their isoelectric point and molecular weight. 2-DGE images often present anomalies due to the acquisition process, such as: diffuse and overlapping spots, and background noise. This study proposes a joint pre-processing framework that combines the capabilities of nonlinear filtering, background correction and image normalization techniques for pre-processing 2-DGE images. Among the most important, joint nonlinear diffusion filtering, adaptive piecewise histogram equalization and multilevel thresholding were evaluated using both synthetic data and real 2-DGE images. Results An improvement of up to 46% in spot detection efficiency was achieved for synthetic data using the proposed framework compared to implementing a single technique of either normalization, background correction or filtering. Additionally, the proposed framework increased the detection of low abundance spots by 20% for synthetic data compared to a normalization technique, and increased the background estimation by 67% compared to a background correction technique. In terms of real data, the joint pre-processing framework reduced the false positives up to 93%. Conclusions The proposed joint pre-processing framework outperforms results achieved with a single approach. The best structure was obtained with the ordered combination of adaptive piecewise histogram equalization for image normalization, geometric nonlinear diffusion (GNDF) for filtering, and multilevel thresholding for background correction.

【 授权许可】

Unknown