Level-Set Segmentation Algorithm Combined with Local Gray Threshold on Image Sequences of Neuron Stem Cells

In optical microscopy imaged time lapse of neuron stem cells image sequences, there exist low contrast ratio between objects and background, adherent and clustered cells' interference. A novel algorithm is presented aiming to solve these problems. It is based on a level-set without the need of re-initialization. After curvature term is added in order to accelerate convergence, iteration terminating condition is changed to measure norm energy in order to decrease complexity. Local gray threshold is combined with the result of curve evolution for clustered cells' separation at last. The presented algorithm is applied in two sequence images of 120 frames. The segmented results show that the algorithm can not only solve the problem of focus excursion but also separate adherent and clustered cells successfully as well as keep cells' shape and location. After compared with watershed and traditional level-set algorithms, this algorithm can improve the success rate to 30%~40%.