the Extraction of Biomedical Structures in Microscopical Images A. Botto

ABSTRACT To overcome the typical difficulties for the extraction of biomedical structures in microscopical images, a knowledge-based system has been designed with the aim of performing a model-driven segmentation towards automatic structure recognition. The work deals with the characterization of cell populations in human embryonal and foetal organs, in particular blood vessels and nervous cells. By using a-priori known description (in linguistic form) about the structures to be detected, a thresholding segmentation is initially used for locating darker pixels (markers). Then gradient extraction, region-growing, or other techniques are invoked around the area bounding markers. The computation of some specific attributes of the analyzed areas drives the recognition process allowing to maintain regions where structures are detected, and discard regions where structure presence is not verified. The segmentation and recognition process is controlled by a production system whose rules are activated on the basis of the input data, the progressive results, and the information (provided by the user) about the structures to be localized. Some results are presented to the user, obtained by changing processing parameters. They correspond to recognized maps with different reliability factors.