Automatic matching method of protein spots between two gel images

Abstract

The invention relates to the technical field of image registration, more particularly to an automatic matching method of protein spots between two gel images, which is characterized in that the method comprises the following steps that: 1) the protein spots of two gel images are respectively detected and quantified; 2) any of the two gel images is chosen, statistics on the distances between all two adjacent protein spots is collected in a middle area, and the average value of the distances is taken as a threshold value of matched distance; 3) extracting characteristic protein spots of the two gel images; 4) according to the line segment distance and the angle between the characteristic protein spots, the characteristic protein spots between the two gel images are matched in combination with a statistic method; 5) local coordinate change is performed by the adjacent matched characteristic protein spots so that the protein spots between the two gel images are changed in the same coordinate system; and 6) the protein spots between the two gel images are matched. The method has not only high robustness and precision, but also high intellectual degree.

Description

technical field [0001] The invention relates to the technical field of image registration, in particular to an automatic protein point matching method between two gel images. Background technique [0002] The protein spot analysis method based on gel images is one of the important contents in the field of molecular biology research. The protein spot matching between different gel images is the key link in the molecular analysis of protein spots. It is the extraction and mass spectrometry analysis of differential protein spots in samples. It has broad application prospects in the diagnosis of human cardiovascular diseases, nervous system diseases, infectious diseases, tumors and other major diseases. [0003] The premise of the extraction and mass spectrometry analysis of differential protein spots is the matching of protein spots between the two gel images, and the matching accuracy directly affects the subsequent analysis and disease diagnosis. The images of the same scene...

AI Technical Summary

Problems solved by technology

Traditional feature point matching usually uses dissimilarity measure SSD (sum of square differences) and similarity measure NCC (normalized cross correlation—normalized cross-correlation) to match, which works well for loose feature point matching, but The matching effect of dense feature points is not good. Due to the uneven distribution of feature protein points in gel images, traditional matching methods are difficult to apply, which will easily cause mis-matching of feature protein points, and then affect the protein points between the two gel images. matching accuracy

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