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Computer vision for microscopy diagnosis of malaria.

Tek FB, Dempster AG, Kale I - Malar. J. (2009)

Bottom Line: Existing works interpret the diagnosis problem differently or propose partial solutions to the problem.A critique of these works is furnished.In addition, a general pattern recognition framework to perform diagnosis, which includes image acquisition, pre-processing, segmentation, and pattern classification components, is described.

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Affiliation: Applied DSP & VLSI Research Group, University of Westminster, London, UK. boraytek@yahoo.co.uk

ABSTRACT
This paper reviews computer vision and image analysis studies aiming at automated diagnosis or screening of malaria infection in microscope images of thin blood film smears. Existing works interpret the diagnosis problem differently or propose partial solutions to the problem. A critique of these works is furnished. In addition, a general pattern recognition framework to perform diagnosis, which includes image acquisition, pre-processing, segmentation, and pattern classification components, is described. The open problems are addressed and a perspective of the future work for realization of automated microscopy diagnosis of malaria is provided.

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Examples of Giemsa-stained (a) thin and (b) thick blood film smear images, (c) a concentrated (thick) field of a thin blood film smear.
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Figure 3: Examples of Giemsa-stained (a) thin and (b) thick blood film smear images, (c) a concentrated (thick) field of a thin blood film smear.

Mentions: Figure 3 shows examples of stained thin and thick blood film images which contain malarial parasites. As far as this survey is concerned, almost all of the computer vision methods and related studies in the literature use thin blood film smears. Therefore, the discussions presented in this paper are on the thin film analysis works. However, the different requirements of thick blood films are remarked when appropriate. Polymerase chain reaction (PCR) methods are known to be more sensitive and more specific than (manual) microscopy [19-21]. Recent advances in the technique allow high-throughput applications and promote its use in routine diagnosis [22,23]. Mueller et al [24] show that Post-PCR ligase detection reaction fluorescent microsphere assay is more accurate than light microscopy in resolving species in the presence of mixed infections, which are common in the areas where malaria is endemic. PCR-based methods may replace microscopy examination as the gold-standard [20]; however, costs are significantly higher and more expensive instruments [25] are required.


Computer vision for microscopy diagnosis of malaria.

Tek FB, Dempster AG, Kale I - Malar. J. (2009)

Examples of Giemsa-stained (a) thin and (b) thick blood film smear images, (c) a concentrated (thick) field of a thin blood film smear.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2719653&req=5

Figure 3: Examples of Giemsa-stained (a) thin and (b) thick blood film smear images, (c) a concentrated (thick) field of a thin blood film smear.
Mentions: Figure 3 shows examples of stained thin and thick blood film images which contain malarial parasites. As far as this survey is concerned, almost all of the computer vision methods and related studies in the literature use thin blood film smears. Therefore, the discussions presented in this paper are on the thin film analysis works. However, the different requirements of thick blood films are remarked when appropriate. Polymerase chain reaction (PCR) methods are known to be more sensitive and more specific than (manual) microscopy [19-21]. Recent advances in the technique allow high-throughput applications and promote its use in routine diagnosis [22,23]. Mueller et al [24] show that Post-PCR ligase detection reaction fluorescent microsphere assay is more accurate than light microscopy in resolving species in the presence of mixed infections, which are common in the areas where malaria is endemic. PCR-based methods may replace microscopy examination as the gold-standard [20]; however, costs are significantly higher and more expensive instruments [25] are required.

Bottom Line: Existing works interpret the diagnosis problem differently or propose partial solutions to the problem.A critique of these works is furnished.In addition, a general pattern recognition framework to perform diagnosis, which includes image acquisition, pre-processing, segmentation, and pattern classification components, is described.

View Article: PubMed Central - HTML - PubMed

Affiliation: Applied DSP & VLSI Research Group, University of Westminster, London, UK. boraytek@yahoo.co.uk

ABSTRACT
This paper reviews computer vision and image analysis studies aiming at automated diagnosis or screening of malaria infection in microscope images of thin blood film smears. Existing works interpret the diagnosis problem differently or propose partial solutions to the problem. A critique of these works is furnished. In addition, a general pattern recognition framework to perform diagnosis, which includes image acquisition, pre-processing, segmentation, and pattern classification components, is described. The open problems are addressed and a perspective of the future work for realization of automated microscopy diagnosis of malaria is provided.

Show MeSH
Related in: MedlinePlus