GAUSSIAN MIXTURE MODELS FOR SPOTS IN MICROSCOPY USING A NEW SPLIT/MERGE EM ALGORITHM

In confocal microscopy imaging, target objects are labeled with fluorescent markers in the living specimen, and usually appear as spots in the observed images. Spot detection and analysis is therefore an important task but it is still heavily reliant on manual analysis. In this paper, a novel shape modeling algorithm is proposed for automating the detection and analysis of the spots of interest. The algorithm exploits a Gaussian mixture model to characterize the spatial intensity distribution of the spots, and estimates parameters using a novel split-and-merge expectation maximization (SMEM) algorithm. In previous work the split step is random which is an issue for biological analysis where repeatability is important. The new split/merge steps are deterministic, hence more useful, and further do not impact adversely on the optimality of the final result.