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The results of an automated classification of the peripheral blood leukocytes into eight categories are presented. The classification was achieved by means of digital image processing. The categories were: small lymphocytes, medium lymphocytes, large lymphocytes, band neutrophils, segmented neutrophils, eosinophils, basophils, and monocytes. An eight-dimensional multivariate Gaussian classifier was used. The features were extracted from a 50 Ã 50 point digital image. These features were measures of such visual concepts as nuclear size, nuclear shape, nuclear and cytoplasmic texture, cytoplasm color, and cytoplasm colored texture. The data set consisted of 1041 blood cell images and were divided into a training set of 523 cells and an independent testing set of 518 cells. These cells were digitized directly from the blood smear, which was stained with Wright's stain. Twenty different blood smears were used and were collected over a three-year period from 20 people. The "true" classification of the data set was obtained from four experienced hematology technicians. Their performance was compared to the automated classifier both in terms of an absolute classification of cells and in terms of estimating the percentage composition of the population (or the blood cell differential count). The measure of performance used was the percentage error for each class. The mean percentage error for the eight classes in terms of an absolute classification was 8 and 29 percent for the human observers and the automated classiffier, respectively.
Systems, Man and Cybernetics, IEEE Transactions on (Volume:SMC-2 , Issue: 4 )
Date of Publication: Sept. 1972