Article

Malaria Cell Counting Diagnosis within Large Field of View

Authors:

Li-hui Zou,

Jie Chen,

Juan Zhang,

Narciso GarciaAuthors Info & Claims

DICTA '10: Proceedings of the 2010 International Conference on Digital Image Computing: Techniques and Applications

Pages 172 - 177

https://doi.org/10.1109/DICTA.2010.40

Published: 01 December 2010 Publication History

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Abstract

Malaria is one of the most serious parasitic infections of human. The accurate and timely diagnosis of malaria infection is essential to control and cure the disease. Some image processing algorithms to automate the diagnosis of malaria on thin blood smears are developed, but the percentage of parasitaemia is often not as precise as manual count. One reason resulting in this error is ignoring the cells at the borders of images. In order to solve this problem, a kind of diagnosis scheme within large field of view (FOV) is proposed. It includes three steps. The first step is image mosaicing to obtain large FOV based on space-time manifolds. The second step is the segmentation of erythrocytes where an improved Hough Transform is used. The third step is the detection of nucleated components. At last, it is concluded that the counting accuracy of malaria infection within large FOV is finer than several regular FOVs.

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Shambhu SKoundal DDas PHoang VTran-Trung KTurabieh H(2022)Computational Methods for Automated Analysis of Malaria Parasite Using Blood Smear ImagesComputational Intelligence and Neuroscience10.1155/2022/36267262022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/3626726
Jan ZKhan ASajjad MMuhammad KRho SMehmood I(2018)A review on automated diagnosis of malaria parasite in microscopic blood smears imagesMultimedia Tools and Applications10.1007/s11042-017-4495-277:8(9801-9826)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s11042-017-4495-2
Abbas NSaba TMohamad DRehman AAlmazyad AAl-Ghamdi J(2018)Machine aided malaria parasitemia detection in Giemsa-stained thin blood smearsNeural Computing and Applications10.1007/s00521-016-2474-629:3(803-818)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s00521-016-2474-6
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DICTA '10: Proceedings of the 2010 International Conference on Digital Image Computing: Techniques and Applications

December 2010

660 pages

ISBN:9780769542713

Publisher

IEEE Computer Society

United States

Publication History

Published: 01 December 2010

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Shambhu SKoundal DDas PHoang VTran-Trung KTurabieh H(2022)Computational Methods for Automated Analysis of Malaria Parasite Using Blood Smear ImagesComputational Intelligence and Neuroscience10.1155/2022/36267262022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/3626726
Jan ZKhan ASajjad MMuhammad KRho SMehmood I(2018)A review on automated diagnosis of malaria parasite in microscopic blood smears imagesMultimedia Tools and Applications10.1007/s11042-017-4495-277:8(9801-9826)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s11042-017-4495-2
Abbas NSaba TMohamad DRehman AAlmazyad AAl-Ghamdi J(2018)Machine aided malaria parasitemia detection in Giemsa-stained thin blood smearsNeural Computing and Applications10.1007/s00521-016-2474-629:3(803-818)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s00521-016-2474-6
Gopakumar GSwetha MSiva GSubrahmanyam GBatra DBrown MNatarajan V(2016)Automatic detection of Malaria infected RBCs from a focus stack of bright field microscope slide imagesProceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing10.1145/3009977.3010024(1-7)Online publication date: 18-Dec-2016
https://dl.acm.org/doi/10.1145/3009977.3010024

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