Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities

IEEE Access

Hyeonjoon Moon¹  Jin Tae Kwak¹  Irfan Mehmood¹  Zahoor Jan²  Muhammad Sajjad²  Siraj Khan²  Seungmin Rho³  Sung Wook Baik⁴  Khan Muhammad⁴ 

[1] Department of Computer Science and Engineering, Sejong University, Seoul, South Korea;Department of Computer Science, Digital Image Processing Laboratory, Islamia College Peshawar, Pakistan;Department of Media Software, Sungkyul University, Anyang, South Korea;Department of Software, Intelligent Media Laboratory, College of Software Convergence, Sejong University, Seoul, South Korea;

关键词: Healthcare in smart cities;    haematology;    image classification;    image segmentation;    leukocytes classification;    mobile-cloud computing;   

DOI  :  10.1109/ACCESS.2016.2636218

来源: DOAJ

【 摘 要 】

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 time-consuming. A mobile-cloud-assisted detection and classification 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 classification mechanism in the cloud. In the proposed framework, nucleus is first segmented, followed by extraction of texture, statistical, and wavelet features. Finally, the detected WBCs are categorized into five classes: basophil, eosinophil, neutrophil, lymphocyte, and monocyte. Experimental results on numerous benchmark databases validate the effectiveness and efficiency of the proposed system in comparison to the other state-of-the-art schemes.

【 授权许可】

Unknown