An improved watershed algorithm for counting objects in noisy, anisotropic 3-D biological images

Effective 3-D image processing algorithms are presented for automatic counting and analysis of cells in anisotropic 3-D biological images that are collected by laser-scanning confocal microscopes. In these instruments, the x-y resolution is much better than the resolution along the z axis, hence the voxels (pixels in 3-D) are anisotropic. In this work, the images are pre-processed by a 3-D extension of an anisotropic diffusion algorithm, and the resulting images are binarized by a clustering based segmentation algorithm. As a result of binary segmentation, some regions consist of individual objects while others are multi-object clusters. An extension of Vincent and Soille´s watershed algorithm (1991) to anisotropic 3D spaces is used to separate such cell clusters. The watershed algorithm is applied on marker functions that are generated using a combination of 3-D morphological inverse distance functions and 3-D image gradients. Cell measurements, such as volume, average intensity and locations, are calculated on the result of watershed segmentation. This algorithm has been successfully applied to the automated analysis of cell populations from a variety of biological studies involving large numbers of tissue samples