A relational Fuzzy C-Means algorithm for detecting protein spots in two-dimensional gel images

Two-dimensional polyacrylamide gel electrophoresis of proteins is a robust and reproducible technique. It is the most widely used separation tool in proteomics. Current efforts in the field are directed at the development of tools for expanding the range of proteins accessible with two-dimensional gels. Proteomics was built around the two-dimensional gel. The idea that multiple proteins can be analyzed in parallel grew from two-dimensional gel maps. Proteomics researchers needed to identify interested protein spots by examining the gel. This is time consuming, labor extensive and error prone. It is desired that the computer can analyze the proteins automatically by first detecting, then quantifying the protein spots in the 2D gel images. This paper focuses on the protein spot detection and segmentation of 2D gel electrophoresis images. We present a new technique for segmentation of 2D gel images using the Fuzzy C-Means (FCM) algorithm and matching spots using the notion of fuzzy relations. Through the experimental results, the new algorithm was found out to detect protein spots more accurately, then the current known algorithms.