Red blood cell segmentation from SEM images

We present a model based contour tracing approach to the problem of automatically segmenting a scanning electron microscope image of red blood cells. We use a second order polynomial model and a simple Bayesian approach to ensure smooth boundaries, and a postprocess ellipse fitting procedure to cull noise contours. Of all contours detected, 95.7% are correct, with a 0.6% false negative rate, and 4.3% false positive rate on 100 sample images involving more than 11000 red blood cells.