Segmenting Biological Particles in Multispectral Microscopy Images

This paper presents a methodology and results for segmentation of biological particles in multispectral images by learning disparate models from each spectra for pixel classification coupled with contour evolution based on the use of level set theory. Traditional contour models have some limitations on the segmentation of complicated images whose sub-regions consist of multiple components. The segmentation of multispectral images is even a more difficult problem. Our proposed model overcomes these limitations and uses multiple classifiers, each of which solves the problem independently based on its input observations. Each classifier module is trained to detect distinct regions and a higher order decision integrator collects evidence from each of the modules to delineate a final region