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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.

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.

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Stained object classes: in a Giemsa-stained blood film an observed stained object can be a parasite from one of the four species of Plasmodium or a regular blood component such as white blood cell, platelet. Artefact class represents bacteria, spores, crystallized stain chemicals, particles due to dirt, RBC anomalies (e.g. Howell-Jolly bodies, iron deficiency, reticulocytes), and other peripheral blood parasites.
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Figure 2: Stained object classes: in a Giemsa-stained blood film an observed stained object can be a parasite from one of the four species of Plasmodium or a regular blood component such as white blood cell, platelet. Artefact class represents bacteria, spores, crystallized stain chemicals, particles due to dirt, RBC anomalies (e.g. Howell-Jolly bodies, iron deficiency, reticulocytes), and other peripheral blood parasites.

Mentions: Using a microscope, visual detection and identification of the Plasmodium is possible and efficient via a chemical process called staining. A popular stain, Giemsa, slightly colors red blood cells (RBCs) but highlights the parasites, white blood cells (WBC), platelets, and various artefacts (Figure 1). In order to detect the infection it could be sufficient to divide stained objects into two groups such as parasite/non-parasite and differentiate between them. However to specify the infection and to perform a detailed quantification, all four species of Plasmodium at four life-cycle-stages must be differentiated (Figure 2). Despite that the term 'artefact" is not very definitive, any stained object that is not a regular blood component or a parasite is referred here using this term: these include bacteria, spores, crystallized stain chemicals, and particles due to dirt [3]. It must be noted that other peripheral blood parasites and RBC anomalies (e.g. Howell-Jolly bodies, iron deficiency, reticulocytes) are included in this artefact class definition. They could be examined in individual dedicated classes if their identification is also required.


Computer vision for microscopy diagnosis of malaria.

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

Stained object classes: in a Giemsa-stained blood film an observed stained object can be a parasite from one of the four species of Plasmodium or a regular blood component such as white blood cell, platelet. Artefact class represents bacteria, spores, crystallized stain chemicals, particles due to dirt, RBC anomalies (e.g. Howell-Jolly bodies, iron deficiency, reticulocytes), and other peripheral blood parasites.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Stained object classes: in a Giemsa-stained blood film an observed stained object can be a parasite from one of the four species of Plasmodium or a regular blood component such as white blood cell, platelet. Artefact class represents bacteria, spores, crystallized stain chemicals, particles due to dirt, RBC anomalies (e.g. Howell-Jolly bodies, iron deficiency, reticulocytes), and other peripheral blood parasites.
Mentions: Using a microscope, visual detection and identification of the Plasmodium is possible and efficient via a chemical process called staining. A popular stain, Giemsa, slightly colors red blood cells (RBCs) but highlights the parasites, white blood cells (WBC), platelets, and various artefacts (Figure 1). In order to detect the infection it could be sufficient to divide stained objects into two groups such as parasite/non-parasite and differentiate between them. However to specify the infection and to perform a detailed quantification, all four species of Plasmodium at four life-cycle-stages must be differentiated (Figure 2). Despite that the term 'artefact" is not very definitive, any stained object that is not a regular blood component or a parasite is referred here using this term: these include bacteria, spores, crystallized stain chemicals, and particles due to dirt [3]. It must be noted that other peripheral blood parasites and RBC anomalies (e.g. Howell-Jolly bodies, iron deficiency, reticulocytes) are included in this artefact class definition. They could be examined in individual dedicated classes if their identification is also 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