The two-dimensional electrophoresis (2-DE) method is widely used in proteomics to separate thousands of proteins in a sample. Given a set of gel images of the same kind, a spot-matching operation identifies the spot of the same protein in each 2-DE gel image. However, since 2-DE gel images are very sensitive to various experimental conditions, spots observed in a 2-DE gel image involve inherent errors such as noises and irregular geometric distortions. Consequently, the accuracy of spot-matching is intensively influenced by these errors. This paper proposes a spot-matching framework to improve the accuracy of 2-DE gel image analysis. The framework includes an automatic landmark extraction module that uses a pattern model of positional and intensive distortions identified in the learning set of gel images. Subsequently, a probabilistic matching adjustment module is employed to produce more reliable spot-matching results based on the selected landmark spots in the first module. In order to confirm the synergy effects of integrating these two modules, the improved accuracy of the proposed framework is analyzed through various experiments on real 2-DE gel images of human liver tissue.