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Diagnosis support of sickle cell anemia by classifying red blood cell shape in peripheral blood images

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Abstract

Red blood cell (RBC) deformation is the consequence of several diseases, including sickle cell anemia, which causes recurring episodes of pain and severe pronounced anemia. Monitoring patients with these diseases involves the observation of peripheral blood samples under a microscope, a time-consuming procedure. Moreover, a specialist is required to perform this technique, and owing to the subjective nature of the observation of isolated RBCs, the error rate is high. In this paper, we propose an automated method for differentially enumerating RBCs that uses peripheral blood smear image analysis. In this method, the objects of interest in the image are segmented using a Chan-Vese active contour model. An analysis is then performed to classify the RBCs, also called erythrocytes, as normal or elongated or having other deformations, using the basic shape analysis descriptors: circular shape factor (CSF) and elliptical shape factor (ESF). To analyze cells that become partially occluded in a cluster during sample preparation, an elliptical adjustment is performed to allow the analysis of erythrocytes with discoidal and elongated shapes. The images of patient blood samples used in the study were acquired by a clinical laboratory specialist in the Special Hematology Department of the “Dr. Juan Bruno Zayas” General Hospital in Santiago de Cuba. A comparison of the results obtained by the proposed method in our experiments with those obtained by some state-of-the-art methods showed that the proposed method is superior for the diagnosis of sickle cell anemia. This superiority is achieved for evidenced by the obtained F-measure value (0.97 for normal cells and 0.95 for elongated ones) and several overall multiclass performance measures. The results achieved by the proposed method are suitable for the purpose of clinical treatment and diagnostic support of sickle cell anemia.

We present a new method to obtain erythrocyte shape classification using peripheral blood smear sample images. The aim of the method is to segment the cells, to separate clusters and classify cells (circulars, elongated and others). We compared our method with state-of the-art. Results showed that our method with is superior for the diagnosis support of sickle cell anemia.

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Notes

  1. The authors thank the authors of [8], who executed their method to provide the data collected in Table 1, since the data collected in Table 2 of [8] correspond with the initialization of the textures but not with the level set initialization.

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Funding

We acknowdledge the Ministerio de Economía, Industria y Competitividad (MINECO), the Agencia Estatal de Investigación (AEI) and the European Regional Development Funds\break (ERDF) for its support to the projects TIN2016-81143-R (MINECO/{\break}AEI/ERDF, EU) and TIN2016-75404-P (MINECO/AEI/ERDF, EU), and the support of the Government of the Balearic Islands and the University of the Balearic Islands to the projects OCDS-CUD2016/01 and OCDS-CUD2017/05. We also thank the Mathematics and Computer Science Department at the University of the Balearic Islands for its support. The work of the team belonging to the Universidad de Oriente is also subsidized by the Belgian Development Cooperation through VLIR-UOS (Flemish Interuniversity Council-University Cooperation for Development) in the context of the Institutional University Cooperation programme with Universidad de Oriente.

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Authors and Affiliations

  1. Departamento de Computación, Facultad de Ciencias Naturales y Exactas, Universidad de Oriente, Santiago de Cuba, Cuba

    Wilkie Delgado-Font, Miriela Escobedo-Nicot & Silena Herold-Garcia

  2. Balearic Islands Health Research Institute (IdISBa), Soft Computing, Image Processing and Aggregation (SCOPIA) Research Group, Department of Mathematics and Computer Science, Universitat de les Illes Balears, Palma, Spain

    Manuel González-Hidalgo

  3. Research Institute of Health Sciences (IUNICS), Computer Graphics and Vision and AI Group (UGiVIA), Department of Mathematics and Computer Science, Universitat de les Illes Balears, Palma, Spain

    Antoni Jaume-i-Capó

  4. Balearic Islands Health Research Institute (IdISBa), Computational Biology and Bioinformatics (BIOCOM) Research Group, Department of Mathematics and Computer Science, Universitat de les Illes Balears, Palma, Spain

    Arnau Mir

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  1. Wilkie Delgado-Font
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Correspondence to Antoni Jaume-i-Capó.

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Delgado-Font, W., Escobedo-Nicot, M., González-Hidalgo, M. et al. Diagnosis support of sickle cell anemia by classifying red blood cell shape in peripheral blood images. Med Biol Eng Comput 58, 1265–1284 (2020). https://doi.org/10.1007/s11517-019-02085-9

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