Segmentation of Clustered Nuclei With Shape Markers and Marking Function

We present a method to separate clustered nuclei from fluorescence microscopy cellular images, using shape markers and marking function in a watershed-like algorithm. Shape markers are extracted using an adaptive H-minima transform. A marking function based on the outer distance transform is introduced to accurately separate clustered nuclei. With synthetic images, we quantitatively demonstrate the performance of our method and provide comparisons with existing approaches. On mouse neuronal and Drosopbila cellular images, we achieved 6%-7% improvement of segmentation accuracies over earlier methods.