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

Solange Doumun 1, 2, 3 Sophie Dabo-Niang 4 Jérémie Zoueu 3
4 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille
Abstract : 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 %.
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https://hal.inria.fr/hal-03133812
Contributor : Sophie Dabo-Niang Connect in order to contact the contributor
Submitted on : Sunday, February 7, 2021 - 11:35:37 AM
Last modification on : Tuesday, January 4, 2022 - 6:28:52 AM

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Solange Doumun, Sophie Dabo-Niang, Jérémie Zoueu. Detection and segmentation of erythrocytes in multispectral label-free blood smear images for automatic cell counting. Journal of Spectral Imaging, IM Publications, 2020, 9 (Article ID a10), ⟨10.1255/jsi.2020.a10⟩. ⟨hal-03133812⟩

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