Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities
Smart cities are a future reality for municipalities around the world. Healthcare services play a
vital role in the transformation of traditional cities into smart cities. In this paper, we present a ubiquitous and
quality computer-aided blood analysis service for the detection and counting of white blood cells (WBCs)
in blood samples. WBCs also called leukocytes or leucocytes are the cells of the immune system that are
involved in protecting the body against both infectious disease and foreign invaders. Analysis of leukocytes
provides valuable information to medical specialists, helping them in diagnosing different important hematic
diseases, such as AIDS and blood cancer (Leukaemia). However, this task is prone to errors and can be timeconsuming.
A mobile-cloud-assisted detection and classi cation of leukocytes from blood smear images
can enhance accuracy and speed up the detection of WBCs. In this paper, we propose a smartphone-based
cloud-assisted resource aware framework for localization of WBCs within microscopic blood smear images
using a trained multi-class ensemble classi cation mechanism in the cloud. In the proposed framework,
nucleus is rst segmented, followed by extraction of texture, statistical, and wavelet features. Finally,
the detected WBCs are categorized into ve classes: basophil, eosinophil, neutrophil, lymphocyte, and
monocyte. Experimental results on numerous benchmark databases validate the effectiveness and ef ciency
of the proposed system in comparison to the other state-of-the-art schemes.
KeyWords
Healthcare in smart cities, haematology, image classi cation, image segmentation,
leukocytes classi cation, mobile-cloud computing, medical image analysis.
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