3D biological object detection and labeling in multidimensional microscopy imaging

One essential assumption used in object detection and labeling by imaging is that the photometric properties of the object are homogeneous. This homogeneity 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 3D biological object detection and labeling. The developed approach is based on a statistical, non-parametric framework. The image is first divided into regular non-overlapped regions and each region is evaluated according to a general photometric variability model. The regions not consistent with this model are considered as aberrations 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 the same object are merged under a set of hypotheses. These hypotheses are generated by taking into account photometric and geometric properties of objects and the merging is realized according to an iterative algorithm. The approach has been applied in investigations of the spatial distribution of nuclei on colonic glands of rats observed with with help of confocal fluorescence microscopy.