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One essential assumption used in object detection and labeling by imaging is that the photometric properties of the object are homogeneous. This homogeneousness requirement is often violated in microscopy imaging. Classical methods are usually of high computational cost and fail to give a stable solution. This paper presents a low computational complexity and robust method for three-dimensional (3-D) biological object detection and labeling. The developed approach is based on a statistical, nonparametric framework. Image is first divided into regular nonoverlapped regions and each region is evaluated according to a general photometric variability model. The regions not consistent with this model are considered as aberration in the data and excluded from the analysis procedure. Simultaneously, the interior parts of the object are detected, they correspond to regions where the supposed model is valid. In the second stage, the valid regions from a same object are merged together depending on a set of hypotheses. These hypotheses are generated by taking into account photometric and geometric properties of objects of interest and the merging is achieved according to an iterative algorithm. The approach has been applied in investigations of spatial distribution of nuclei within colonic glands of rats observed with the help of confocal fluorescence microscopy.