Leukocyte image analysis in the diff3 system

Abstract The diff3 System uses image processing and pattern recognition techniques to automatically analyze normal and abnormal white blood cells in a blood smear. The system consists of a spinner which creates a monolayer of cells on a glass slide, a stainer utilizing Wright's stain, the reagents to support the spinner and stainer, and an analyzer for automatic slide handling, analysis and report generation. The analyzer incorporates a wide range of image processing functions, including the generation and storage of gray scale image data, whole-field and partial-field image histogramming, and high-order binary image texture analysis and image transformation using the Golay processor (GLOPR). This paper describes the manner in which these hardware capabilities are used for white cell acquisition, scene segmentation and feature analysis. It concludes with some examples of texture extraction which illustrate the power of the Golay processor as a tool for image analysis.

[1]  C. Heckler,et al.  An automated blood smear analysis system. Clinical experience and performance. , 1980, American journal of clinical pathology.

[2]  K Preston Automation of the analysis of cell images. , 1980, Analytical and quantitative cytology.

[3]  B H Mayall,et al.  Focus-assist device for a flying-spot microscope. , 1973, IEEE transactions on bio-medical engineering.

[4]  R. Schoentag,et al.  Evaluation of an automated blood smear analyzer. , 1979, American journal of clinical pathology.

[5]  M. Ingram,et al.  Semiautomatic preparation of coverglass blood smears using a centrifugal device. , 1969, American Journal of Clinical Pathology.

[6]  H. Harms,et al.  Computer aided analysis of chromatin network and basophil color for differentiation of mononuclear peripheral blood cells. , 1979, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[7]  Kendall Preston,et al.  Feature Extraction by Golay Hexagonal Pattern Transforms , 1971, IEEE Transactions on Computers.

[8]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[9]  A V Kulkarni Effectiveness of feature groups for automated pairwise leukocyte class discrimination. , 1979, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[10]  J. E. Green,et al.  A practical application of computer pattern recognition research: the Abbott ADC-500 differential classifier. , 1979, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[11]  Melvin N. Miller,et al.  Design and Clinical Results of Hematrak: An Automated Differential Counter , 1976, IEEE Transactions on Biomedical Engineering.

[12]  A. Rosenfeld,et al.  Visual texture analysis , 1970 .

[13]  M. Nourbakhsh,et al.  An evaluation of blood smears made by a new method using a spinner and diluted blood. , 1978, American journal of clinical pathology.

[14]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

[15]  S. Levialdi,et al.  Basics of cellular logic with some applications in medical image processing , 1979, Proceedings of the IEEE.

[16]  E. Vastola,et al.  A description by computer of texture in Wright-stained leukocytes. , 1974, Acta cytologica.

[17]  K. Fu,et al.  Automated classification of blood cell neutrophils. , 1977, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[18]  J. F. Brenner,et al.  AUTOMATED CLASSIFICATION OF NORMAL AND ABNORMAL LEUKOCYTES , 1974, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[19]  Megla Gk The LARC automatic white blood cell analyzer. , 1973 .

[20]  Marcel J. E. Golay,et al.  Hexagonal Parallel Pattern Transformations , 1969, IEEE Transactions on Computers.