Applying watershed algorithms to the segmentation of clustered nuclei.

By Norberto Malpica, C. Ortiz de Solórzano, Juan José Vaquero, Andrés Santos, I. Vallcorba, J. M. García-Sagredo, Francisco del Pozo. 1998

Cluster division is a critical issue in fluorescence microscopy-based analytical cytology when preparation protocols do not provide appropriate separation of objects. Overlooking clustered nuclei and analyzing only isolated nuclei may dramatically increase analysis time or affect the statistical validation of the results. Automatic segmentation of clustered nuclei requires the implementation of specific image segmentation tools. Most algorithms are inspired by one of the two following strategies: 1) cluster division by the detection of internuclei gradients; or 2) division by definition of domains of influence (geometrical approach). Both strategies lead to completely different implementations, and usually algorithms based on a single view strategy fail to correctly segment most clustered nuclei, or perform well just for a specific type of sample. An algorithm based on morphological watersheds has been implemented and tested on the segmentation of microscopic nuclei clusters. This algorithm provides a tool that can be used for the implementation of both gradient- and domain-based algorithms, and, more importantly, for the implementation of mixed (gradient- and shape-based) algorithms. Using this algorithm, almost 90% of the test clusters were correctly segmented in peripheral blood and bone marrow preparations. The algorithm was valid for both types of samples, using the appropriate markers and transformations.

Subfields: Computer Vision and Pattern Recognition, Medical Imaging

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Citations in the corpus

Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections (2004, methodology): “More than one seed per foreground object will in many methods (i.e. Meyer & Beucher, 1990) result in background seeds passing through foreground components, leading to incorrect segmentation results.”
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