A hierarchical model-based framework for segmenting embedded fluorescence biological targets

Addresses the problem of detecting small regions of interest embedded in larger areas of interest as imaged by a fluorescence imaging system. The application exhibits the variation observed in biological specimens, which are primarily shape and intensity uncertainties. The presence of these variability together with uneven illumination and the lack of a global model, implies that a reliable nonparametric technique should be used to detect the objects of interest. The detection task is formulated as a two step hierarchical approach which integrates both parametric and nonparametric techniques. The image as a whole is considered as a slowly varying multi-modal Gaussian field. The classification of which is obtained through the expectation maximisation algorithm, and a spatially smoother segmentation is accomplished by using a Gibbsian segmenter. Shape deformation constraints retain only the so-called valid objects. A similar approach is employed in the second step, where objects within the already detected objects are identified