Autonomous sub-image matching for two-dimensional electrophoresis gels using MaxRST algorithm

Abstract

Matching two-dimensional electrophoresis (2-DE) gel images typically generates a bottleneck in the automated protein analysis, and image distortion and experimental variation, which reduce the matching accuracy. However, conventional matching schemes only compare two complete images, and landmark selection and registration procedures are rather time-consuming. This work presents a novel and robust Maximum Relation Spanning Tree (MaxRST) algorithm, in which an autonomous sub-image matching method does not require registering or manual selection of landmarks. The 2D gel images are represented graphically. Image features are then quantitatively extracted regardless of image size. Similarity between a sub-image and large image is then determined based on Gaussian similarity measurement inspired by fuzzy method, thereby increasing the accuracy of fractional matching. The proposed autonomous matching algorithm achieves an accuracy of up to 97.29% when matching 627 2-DE gel test images. In addition to accommodating image rotation, reversals, shape deformation and intensity changes, the proposed algorithm effectively addresses the sub-image mapping problem and was analyzed thoroughly using a large dataset containing 4629 images. The contributions of this work are twofold. First, this work presents a novel MaxRST strategy and autonomous matching method that does not require manual landmark selection. Second, the proposed method, which extends 2-DE gel matching to query sub-image and a database containing large sets of images, can be adopted for mapping and locating, and to compare small gel images with large gel images with robustness and efficiency.