A Marker-Free Watershed Approach for 2D-GE Protein Spot Segmentation

Abstract

Two-dimensional gel electrophoresis (2D-GE) is the key technique in large-scale protein identification from complex protein mixtures. The 2D-GE images, which represent protein signals as spots of various intensities and sizes, may yield a lot of information that can help the biologists for exploring the elements affecting human health. Automatic analysis for the gel images can help saving time and labor for biologist in identifying and matching the proteins across the 2D-GE images in which protein spot segmentation is a critical step. In this paper, we present a novel approach for protein spot detection, which is a marker-free watershed that does not require specification of predefined markers for the process of finding watershed contour lines. This approach includes a selective nonlinear filter and pixel intensity distribution analysis for removing local minima which causes over-segmentation when applying watershed transform. It then superimposes those true minima over the reconstructed gradient image before applying watershed transform for spot segmentation. The effectiveness of this marker-free approach was experimentally comparable with other methods.