Hideyuki HORIUCHI’s research while affiliated with Hitachi, Ltd. and other places

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%.

A prototype of automated blood cell analyzer has been developed. The process is performed by the pattern recognition method. Four types of examinations are automatically operated : 1) differential count of normal and abnormal white blood cells, 2) morphological analysis of red blood cells, 3) numerical platelet estimates and 4) reticulocyte count (a slide preparation differing from other three). The WBC differential count is the essential of the process and applies an adaptive and hierarchical classifier.fw Here, the word “adaptive” means self compensation using standard cells in each sample. The standard cells are automatically picked up from input cells. The WBC differential count is processed at the rate of one sample per minute. Net discriminating rate of normal WBC differential count is better than 98%.