Segmentation of Rotavirus-A Particles in Microscopic Images Based on Feature Fusion in Active Contour Model

Abstract The segmentation is the prominent stage in image processing, where a significant commitment is made during automated analysis by delineating structures of interest and discriminating them from background. This separation, which is generally effortless and swift for the human visual system, can become a considerable challenge in algorithm development. In many cases, the segmentation approach dictates the outcome of the entire analysis, since measurements and other processing steps are based on segmented regions. The objective of the present study is to develop an automatic tool to identify and classify the Rotavirus-A particles in digital microscopic images based on fusion of gray level co-occurrence matrix (GLCM) and Gabor features in active contour model. The Geometric features are used to identify and classify the Rotavirus-A particle