Texton-based segmentation and classification of human embryonic stem cell colonies using multi-stage Bayesian level sets

We present a texton-based, multi-stage Bayesian level set algorithm which we use to segment colony images of hESC and their derivatives. We extend our previous research segmenting stem cells according to multiresolution texture methods to accommodate colonies and tissues with diffuse and varied textures via a filter bank approach similar to the MR8. Texture features computed for test images are classified via comparison with learned sets of class-specific textural primitives, known as textons. Encompassing this texture model is the new Bayesian level set algorithm, which smoothes and regularizes classification similar to level sets but is simpler in its probabilistic implementation. The resulting algorithm accurately and automatically classifies images of pluripotent hESC and trophectoderm colonies for high-content screening applications.