Open AccessMobile based Automated Complete Blood Count (Auto-CBC) Analysis System from Blood Smeared Image
Author(s) -
Cham Ying Kit,
Razali Tomari,
Wan Nurshazwani Wan Zakaria,
Norzila Othman,
Syadia Nabilah Mohd Safuan,
Jacqueline Ang Jie Yi,
Nicholas Tan Chun Sheng
Publication year - 2017
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v7i6.pp3020-3029
Subject(s) - zoom , complete blood count , computer science , magnification , computer vision , artificial intelligence , blood smear , microscope , blood film , biomedical engineering , computer graphics (images) , medicine , pathology , optics , physics , malaria , gastroenterology , lens (geology)
Blood cells diagnosis is becoming essential to ensure a proper treatment can be proposed to a blood related disease patient. In current research trending, automated complete blood count analysis system is required for pathologists or researchers to count the blood cells from the blood smeared images. Hence, a portable mobile-based complete blood count (CBC) analysis framework with the aid of microscope is proposed, and the smartphone camera is mounted to the viewing port of the light microscope by adding a smartphone support. Initially, the blood smeared image is acquired from a light microscope with objective zoom of 100X magnifications view the eyepiece zoom of 10X magnification, then captured by the smartphone camera. Next, the areas constitute to the WBC and RBC are extracted using combination of color space analysis, threshold and Otsu procedure. Then, the number of corresponding cells are counted using topological structural analysis, and the cells in clumped region is estimated using Hough Circle Transform (HCT) procedure. After that, the analysis results are saved in the database, and shown in the user interface of the smartphone application. Experimental results show the developed system can gain 92.93% accuracy for counting the RBC whereas 100% for counting the WBC.