Supervised Classification of White Blood Cells by Fusion of Color Texture Features and Neural Network

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Nucleus segmentation is one of important steps in the automatic white blood cell differential counting. In this paper, we proposed a technique to segment images of the nucleus. We analyze a set of white-blood-cell-nucleus-based features using color fuzzy texture spectrum (Base 5). We applied artificial neural network for classification. We compared the results with moment based features. The classification performances are evaluated by class wise classification rates. The results show that the features using nucleus alone could be utilized to achieve a classification rate of 99.05% on the test sets.


Blood cell count--Automation; Image analysis--Data processing; Leucocytes; Neural networks (Computer science)


Biomedical | Biomedical Engineering and Bioengineering | Cells | Electrical and Computer Engineering | Medicine and Health Sciences


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