Protein Spot Matching in Two Dimensional Gel Electrophoresis Images Based on Probabilistic Models

Matching of the protein spots in two dimensional gel electrophoresis (2DGE) images is a main process of analyzing these images. Due to the challenges of 2DGE images such as the presence of noise and artifacts, the matching of protein spots is performed under human supervision. This supervision involves human errors. Therefore, in this work a new automated algorithm has been proposed for spot matching in 2DGE images which is based on a probabilistic model. Due to the complexities of the proposed model, the Variational Bayes has been used to solve the equations of the model. The performance of the proposed algorithm has been evaluated on real and synthetic 2DGE images with some statistical criteria. Protein spots in real image dataset have been matched by the proposed method with angular error of 0.05 and end point error of 1.46 and in synthetic image dataset with angular error of 0.13 and end point error of 0.90. These results reveal higher precision and effectiveness and lower matching error of the proposed method.