Classification of white blood cell types from microscope images: Techniques and challenges

Free to read on publisher website White blood cells (WBC) play a significant role in the immune system by protecting the body from infectious disease and foreign invaders. Therefore, an automatic identification of WBC from microscopic images is an essential importance to help the haematologist in diagnosing diseases, such as leukemia, AIDS, and certain types of blood cancer. Analysis of WBC structure from microscopic images and classification of cells into types and sub-types are challenging because of variations in maturation stage, and intra-class variations of the cell shape in images due to using different acquisition and staining processes. Considering the great interest in the community of health, hematology and medical imaging, this chapter reviews a wide range of state-of-the-art approaches in the WBC classification task. Different steps including image aquistion, image enhancement, image segmentation, feature extraction, classification and evaluation will be presented as shown in Fig-1. We first provide an overview of the structure of WBCs, the types and sub–types of WBC, and their features, including the shape of nuclei, size, function and colour. Next, we detail the process of the identification of WBC in images, including image acquisition and consideration of the effect of staining to visualize changes in the colour and shape of the nucleus. We then provide a survey of the recent history (since 2005) up to current state-of-the-art in automated identification of WBCs, including techniques such as image processing, signal processing, pattern recognition and deep learning techniques. We later discuss the challenges including illumination variations, changes in size and location, different maturation stages, shape, rotation, and background variations. The performance of the current techniques with respect to these challenges is evaluated. This survey will help researchers to address these challenges in future work and in the further investigation of detection, feature extraction and classification of WBCs.

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