Automatic Detection and Quantification of WBCs and RBCs Using Iterative Structured Circle Detection Algorithm

Segmentation and counting of blood cells are considered as an important step that helps to extract features to diagnose some specific diseases like malaria or leukemia. The manual counting of white blood cells (WBCs) and red blood cells (RBCs) in microscopic images is an extremely tedious, time cons...

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Veröffentlicht in:Computational and mathematical methods in medicine Jg. 2014; H. 2014; S. 1 - 17
Hauptverfasser: Alomari, Yazan M., Sheikh Abdullah, Siti Norul Huda, Zaharatul Azma, Raja, Omar, Khairuddin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cairo, Egypt Hindawi Puplishing Corporation 01.01.2014
Hindawi Publishing Corporation
Schlagworte:
Algorithms
Blood Cell Count
Computer Simulation
Erythrocytes - cytology
Humans
Image Processing, Computer-Assisted
Leukocytes - cytology
Microscopy - methods
Regression Analysis
Reproducibility of Results
Software
ISSN:1748-670X, 1748-6718, 1748-6718
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Zusammenfassung:Segmentation and counting of blood cells are considered as an important step that helps to extract features to diagnose some specific diseases like malaria or leukemia. The manual counting of white blood cells (WBCs) and red blood cells (RBCs) in microscopic images is an extremely tedious, time consuming, and inaccurate process. Automatic analysis will allow hematologist experts to perform faster and more accurately. The proposed method uses an iterative structured circle detection algorithm for the segmentation and counting of WBCs and RBCs. The separation of WBCs from RBCs was achieved by thresholding, and specific preprocessing steps were developed for each cell type. Counting was performed for each image using the proposed method based on modified circle detection, which automatically counted the cells. Several modifications were made to the basic (RCD) algorithm to solve the initialization problem, detecting irregular circles (cells), selecting the optimal circle from the candidate circles, determining the number of iterations in a fully dynamic way to enhance algorithm detection, and running time. The validation method used to determine segmentation accuracy was a quantitative analysis that included Precision, Recall, and F-measurement tests. The average accuracy of the proposed method was 95.3% for RBCs and 98.4% for WBCs.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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Academic Editor: Shengyong Chen
ISSN:1748-670X
1748-6718
1748-6718
DOI:10.1155/2014/979302