This paper proposes a fully automated method for annotating confocal microscopy images, through organic component detection and segmentation. The organic component detection is performed through adaptive segmentation using a two-level Otsu’s Method. Two probabilistic classifiers then analyze the detected regions, as to how many components may constitute each one. The first of these employs rule-based reasoning centered on the decreasing harmonic patterns observed in the region area density functions. The second one consists of a Support Vector Machine trained with features derived from the log likelihood ratios of incrementally Gaussian mixture modeling detected regions. The final step pairs the identified cellular and parasitic components, computing the standard infection ratios on biomedical research. Results indicate the proposed method is able to perform the identification and annotation processes on par with expert human subjects, constituting a viable alternative to the traditional manual approach.