Akihide HASHIZUME’s research while affiliated with Hitachi, Ltd. and other places

This paper discusses the classification of blood cell images. The image input method using the single CCD color TV camera and the special color compensation filter, as well as the method of region segmentation for the blood cell images, are described. The following elaborations are made in the image input stage to separate the red blood cell and the white blood cell images in a stable way using optical means. (1) The bluish green light near 450 ∼ 500 nm is cut off from the white light. (2) The accompanying light imbalance between the blue light and the green/red light is compensated by the special color compensation filter. In the (region) segmentation of the blood cell images, a logical operation is developed in which the region of the red blood cell is extracted based on the binary images obtained by the threshold processing of the subtracted image. Using those methods, 59 blood cell images are processed and a satisfactory segmentation result is obtained.

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%.

This paper describes a classification logic to classify a neutrophil either as the band neutrophil or the segmented neutrophil. In a typical case, a band neutrophil has a horseshoe-shaped nucleus while a segmented neutrophil has separated nuclei. So it is very easy to distinguish one from the other. But in the case where nuclei are overlapped or touching, it is difficult to distinguish one from the other. On the other hand, the ratio of the number of band neutrophils to that of the segmented ones is low in normal samples. In the case that the above mentioned ratio is extremely high, the correlation between the counting of the technologist and that of the system might become worse if the fixed classification logic is applied. We developed an adaptive feedback logic for the neutrophil classification, which has three logics. The first is the logic to classify the typical band-form or the typical segmented-form. The second is the logic to presume the ratio of the number of band neutrophils to that of segmented ones in the slide from the number of the typical band class, that of typical segmented class and the total number of neutrophil class, all of which are obtained by automated classification logic. The third is the logic to classify the nontypical form neutrophil into two classes in the discriminant space where discriminant threshold is selected by the ratio presumed in the second logic. This adaptive feedback logic improved the correlation between the counting of the technologist and that of the system in various kinds of samples.

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%.