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ARAM: an automated image analysis software to determine rosetting parameters and parasitaemia in Plasmodium samples.

Kudella PW, Moll K, Wahlgren M, Wixforth A, Westerhausen C - Malar. J. (2016)

Bottom Line: The obtained results are compared with standardized manual analysis.Automated rosetting analyzer for micrographs analyses 25 cell objects per second reliably delivering identical results compared to manual analysis.The second, non-malaria specific, analysis mode of ARAM offers the functionality to detect arbitrary objects.

View Article: PubMed Central - PubMed

Affiliation: Experimental Physics I, University of Augsburg, Universitätsstraße 1, Augsburg, Germany.

No MeSH data available.


Related in: MedlinePlus

Cell density error. a Original image: the cell density is so high, that the non parasitized RBC are in contact and cannot be distinguished by the algorithm. b Analysed image: the cell agglomerate is detected as one object
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Fig7: Cell density error. a Original image: the cell density is so high, that the non parasitized RBC are in contact and cannot be distinguished by the algorithm. b Analysed image: the cell agglomerate is detected as one object

Mentions: The most important preparation parameter is the cell density on the micrographs. Hence, one has to account for a suitable haematocrit: if cells are too close to a cluster but not part of it they may be mistakenly detected as part of the rosette. To reduce this error the cell dilution of the specimen has to be chosen accordingly as illustrated in Fig. 7. Here the cell density is so high that non-bound RBC are in contact, leading to problems in single cell identification. This implies the intrinsic limitation of the presented algorithm.Fig. 7


ARAM: an automated image analysis software to determine rosetting parameters and parasitaemia in Plasmodium samples.

Kudella PW, Moll K, Wahlgren M, Wixforth A, Westerhausen C - Malar. J. (2016)

Cell density error. a Original image: the cell density is so high, that the non parasitized RBC are in contact and cannot be distinguished by the algorithm. b Analysed image: the cell agglomerate is detected as one object
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4835829&req=5

Fig7: Cell density error. a Original image: the cell density is so high, that the non parasitized RBC are in contact and cannot be distinguished by the algorithm. b Analysed image: the cell agglomerate is detected as one object
Mentions: The most important preparation parameter is the cell density on the micrographs. Hence, one has to account for a suitable haematocrit: if cells are too close to a cluster but not part of it they may be mistakenly detected as part of the rosette. To reduce this error the cell dilution of the specimen has to be chosen accordingly as illustrated in Fig. 7. Here the cell density is so high that non-bound RBC are in contact, leading to problems in single cell identification. This implies the intrinsic limitation of the presented algorithm.Fig. 7

Bottom Line: The obtained results are compared with standardized manual analysis.Automated rosetting analyzer for micrographs analyses 25 cell objects per second reliably delivering identical results compared to manual analysis.The second, non-malaria specific, analysis mode of ARAM offers the functionality to detect arbitrary objects.

View Article: PubMed Central - PubMed

Affiliation: Experimental Physics I, University of Augsburg, Universitätsstraße 1, Augsburg, Germany.

No MeSH data available.


Related in: MedlinePlus