Abstract
This paper presents a new white blood cell classification system for the recognition of five types of white blood cells. We propose a new segmentation algorithm for the segmentation of white blood cells from smear images. The core idea of the proposed segmentation algorithm is to find a discriminating region of white blood cells on the HSI color space. Pixels with color lying in the discriminating region described by an ellipsoidal region will be regarded as the nucleus and granule of cytoplasm of a white blood cell. Then, through a further morphological process, we can segment a white blood cell from a smear image. Three kinds of features (i.e., geometrical features, color features, and LDP-based texture features) are extracted from the segmented cell. These features are fed into three different kinds of neural networks to recognize the types of the white blood cells. To test the effectiveness of the proposed white blood cell classification system, a total of 450 white blood cells images were used. The highest overall correct recognition rate could reach 99.11% correct. Simulation results showed that the proposed white blood cell classification system was very competitive to some existing systems.
Highlights
The microscopic inspection of blood smears provides diagnostic information concerning patients’ health status
We propose a new approach to implementing an automatic white blood cell classification system
The database of the white blood cells used in the experiments was downloaded from the CellaVision Competency Software Databases which contain slides stained with either a May Grunwald Giemsa or a Wright staining protocol [21]
Summary
The microscopic inspection of blood smears provides diagnostic information concerning patients’ health status. The inspection results of the differential blood count reveal a wide range of important hematic pathologies. The presence of infections, leukemia, and some particular kinds of cancers can be diagnosed based on the results of the classification and the count of white blood cells. The traditional method for the differential blood count is performed by experienced operators. They use a microscope and count the percentage of the occurrence of each type of cell counted within an area of interest in smears. This manual counting process is very tedious and slow. The necessity of an automated differential counting system becomes inevitable
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