Title :

Detection and segmentation of erythrocytes in multispectral label-free blood smear images for automatic cell counting

Author(s) :

Doumun, Mékapeu Solange [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Lille économie management - UMR 9221 [LEM]
Institut National Polytechnique Félix Houphouët-Boigny [Yamoussoukro] [INP-HB]
Dabo-Niang, Sophie [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Zoueu, Jérémie [Auteur]
Institut National Polytechnique Félix Houphouët-Boigny [Yamoussoukro] [INP-HB]

Journal title :

Journal of Spectral Imaging

Publisher :

IM Publications

Publication date :

2020-09-09

English keyword(s) :

multispectral imaging
segmentation
malaria
automatic diagnosis
image analysis

HAL domain(s) :

Mathématiques [math]/Statistiques [math.ST]

English abstract : [en]

In this work we propose an efficient approach to image segmentation for multispectral images of unstained blood films and automatic counting of erythrocytes. Our method takes advantage of Beer–Lambert’s law by using, first, ...
Show more >In this work we propose an efficient approach to image segmentation for multispectral images of unstained blood films and automatic counting of erythrocytes. Our method takes advantage of Beer–Lambert’s law by using, first, a statistical standardisation equation applied to transmittance images, followed by the local adaptive threshold to detect the blood cells and hysteresis contour closing to obtain the complete blood cell boundaries, and finally the watershed algorithm is used. With this method, image pre-processing is not required, which leads to time savings. We obtained the following results that show that our technique is effective, efficient and fast: Precision of 98.47 % and Recall of 98.23 %, a degree of precision (F-Measurement) of 98.34 % and an Accuracy of 96.75 %.Show less >

Language :

Anglais

Popular science :

Non

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL