A novel semi-automatic image processing approach to determine Plasmodium falciparum parasitemia in Giemsa-stained thin blood smears.
about
An image analysis algorithm for malaria parasite stage classification and viability quantificationReliable enumeration of malaria parasites in thick blood films using digital image analysis.High-content live cell imaging with RNA probes: advancements in high-throughput antimalarial drug discovery.Computer vision for microscopy diagnosis of malaria.A semi-automated method for counting fluorescent malaria oocysts increases the throughput of transmission blocking studies.A malaria diagnostic tool based on computer vision screening and visualization of Plasmodium falciparum candidate areas in digitized blood smears.Clinical microbiology informatics.An automatic device for detection and classification of malaria parasite species in thick blood film.Application of principal component analysis to multispectral-multimodal optical image analysis for malaria diagnostics.Automated estimation of parasitaemia of Plasmodium yoelii-infected mice by digital image analysis of Giemsa-stained thin blood smears.Computational microscopic imaging for malaria parasite detection: a systematic review.Blood Smear Image Based Malaria Parasite and Infected-Erythrocyte Detection and Segmentation.Haplotype specific-sequencing reveals MBL2 association with asymptomatic Plasmodium falciparum infection.Effective and cheap removal of leukocytes and platelets from Plasmodium vivax infected blood.VersaCount: customizable manual tally software for cell counting.Hydrophilic-treated plastic plates for wide-range analysis of Giemsa-stained red blood cells and automated Plasmodium infection rate counting.Erythrocyte shape classification using integral-geometry-based methods.Improved light microscopy counting method for accurately counting Plasmodium parasitemia and reticulocytemia.Image analysis approach for development of a decision support system for detection of malaria parasites in thin blood smear images.Image analysis and machine learning for detecting malaria.Parasite and Infected-Erythrocyte Image Segmentation in Stained Blood SmearsA review on automated diagnosis of malaria parasite in microscopic blood smears imagesAutomated microscopy for routine malaria diagnosis: a field comparison on Giemsa-stained blood films in Peru
P2860
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P2860
A novel semi-automatic image processing approach to determine Plasmodium falciparum parasitemia in Giemsa-stained thin blood smears.
description
2008 nî lūn-bûn
@nan
2008 թուականի Մարտին հրատարակուած գիտական յօդուած
@hyw
2008 թվականի մարտին հրատարակված գիտական հոդված
@hy
2008年の論文
@ja
2008年論文
@yue
2008年論文
@zh-hant
2008年論文
@zh-hk
2008年論文
@zh-mo
2008年論文
@zh-tw
2008年论文
@wuu
name
A novel semi-automatic image p ...... msa-stained thin blood smears.
@ast
A novel semi-automatic image p ...... msa-stained thin blood smears.
@en
A novel semi-automatic image p ...... msa-stained thin blood smears.
@nl
type
label
A novel semi-automatic image p ...... msa-stained thin blood smears.
@ast
A novel semi-automatic image p ...... msa-stained thin blood smears.
@en
A novel semi-automatic image p ...... msa-stained thin blood smears.
@nl
prefLabel
A novel semi-automatic image p ...... msa-stained thin blood smears.
@ast
A novel semi-automatic image p ...... msa-stained thin blood smears.
@en
A novel semi-automatic image p ...... msa-stained thin blood smears.
@nl
P2093
P2860
P356
P1433
P1476
A novel semi-automatic image p ...... msa-stained thin blood smears.
@en
P2093
Claudia Kuss
Minh-Tam Le
Peter R Preiser
Timo R Bretschneider
P2860
P2888
P356
10.1186/1471-2121-9-15
P577
2008-03-28T00:00:00Z
P5875
P6179
1017281480