Automatic segmentation and band detection of protein images based on the standard deviation profile and its derivative

Gel electrophoresis has significantly influenced the progress achieved in genetic studies over the last decade. Image processing techniques that are commonly used to analyze gel electrophoresis images require mainly three steps: band detection, band matching, and quantification and comparison. Although several techniques have been proposed to fully automate all steps, errors in band detection and, hence, in quantification are still important issues to address. In order to detect bands, many techniques were used, including image segmentation. In this paper, we present two novel, fully-automated techniques based on the standard deviation and its derivative to perform segmentation and to detect protein bands. Results show that even for poor quality images with faint bands, segmentation and detection are highly accurate.