Abstract
In sickle cell disease the cell morphology analysis is used to diagnose due the deformation of the red blood cell caused by the disease. Previous works used, in images of peripheral blood samples, ellipse adjustment and concave point detection due to the elongated shape of the erythrocyte and obtained good results the detection of cells that were partially occluded in cells’ clusters. In this work, we propose a new algorithm for detecting noteworthy points in the ellipse adjustment and the use of Hidden Markov Model (HMM) for automatic erythrocyte supervised shape classification in peripheral blood samples. Furthermore, in this study we applied a set of constraints to eliminate the image preprocessing step proposed in previous studies. The method was validated using peripheral blood smear samples images with normal and elongated erythrocytes. In all the experiments, in the classification of normal and elongated cells the sensibility was superior to 96 %.
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Acknowledgements
This work was partially supported by the Projects TIN2012-35427, TIN2013-42795-P, with FEDER support, of the Spanish Government, and “XI Convocatoria de Ayudas Para Proyectos de Cooperación Universitaria al Desarrollo 2014 de la UIB”. The authors also thank the Mathematics and Computer Science Department at the University of the Balearic Islands for its support.
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Delgado-Font, W., González-Hidalgo, M., Herold-Garcia, S., Jaume-i-Capó, A., Mir, A. (2016). Erythrocytes Morphological Classification Through HMM for Sickle Cell Detection. In: Perales, F., Kittler, J. (eds) Articulated Motion and Deformable Objects. AMDO 2016. Lecture Notes in Computer Science(), vol 9756. Springer, Cham. https://doi.org/10.1007/978-3-319-41778-3_9
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