Abstract
This paper describes an automated classification algorithm of RBC's morphological abnormalities, which is one of the examinations of the blood cell differentials. As there are many RBCs in the image (which is called multi-object pattern), one of the important problems for this automation is to develope the segmentation of the multi-object pattern. We developed a new method of the segmentation of the multi-object pattern, which was done only by the local operations based on the wave propagation method. Feature extraction is done only by the local operations, too. Moreover, we developed a logic to detect various kinds of morphological abnormalities by the partition of multidimensional space, which is defined by the feature parameters of RBC's. Classification rate of morphological abnormalities by this method is 92.6%.
