Classification of Red Blood Cells From a Geometric Morphometric Study
DOI:
https://doi.org/10.5566/ias.2962Keywords:
cell classification, bending energy, geometric sampling, integral geometry, stereologyAbstract
Sickle cell disease causes the deformation of erythrocytes into sickle cells. The study of this process using digital images of peripheral blood smears can help specialists to quantify the number of deformed cells in order to gauge the severity of the illness. A new method for classifying red blood cells into three categories: healthy, sickle cell disease, and other deformations is proposed. This method does not require obtaining the contour of each cell but instead utilizes information obtained from a small number of points, obtained through appropriate geometric sampling and the use of stereological formulas. The parameters utilized for classification are the bending energy times length (E) and the circular shape factor (F). In normal cells, which exhibit an almost circular shape, these parameters typically have values close to (1,1). To assess the effectiveness of classification using the parameters (E,F), a synthetic curve dataset and a dataset of red blood cells are employed, applying various supervised and unsupervised classification methods.
References
Alzubaidi L, Fadhel MA Al-Shamma O, Zhang J, Duan Y (2022). Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis. Electronics 9:1--18.
Baddeley A, Vedel-Jensen EB (2005). Stereology for Statisticians, 1st ed. Chapman & Hall.
Benaroya H, Han SM, Nagurka M (2005). Probability Models in Engineering and Science, CRC Press, Taylor and Francis Group.
Bischin C, c{T}ălu c{S}, Silaghi-Dumitrescu R, c{T}ălu M, Giovanzana S, Lupac{ș}cu
CA (2012). Computerized morphometric assessment of the human red blood cells treated with cisplatin. Ann Rom Soc Cell Biol 17(2): 105-10.
Canham PB (1970). The Minimum Energy of Bending as a Possible Explanation of the Biconcave Shape of the Human Red Blood Cell. J Theor Biol 26:61--81.
Cruz-Orive LM (2024). Stereology. Theory and Applications, IAM Series, Springer.
Delgado-Font W, Escobedo-Nicot M, González-Hidalgo M, Herold-García S, Jaume-i-Capó A, Mir A (2020). Diagnosis support of sickle cell anemia by classifying red blood cell shape in peripheral blood images. Med Biol Eng Comput 58:1265--84.
Epifanio I, Gual-Arnau X, Herold S (2020). Morphological analysis of cells by means of an elastic metric in the shape space. Image Anal Stereol 39(1):13--23.
Gómez AI, Cruz M, Cruz-Orive LM (2016). On the precision of curve length estimation in the plane. Image Anal Stereol 35(1):1--14.
Gual-Arnau X, Ibáñez Gual MV, Monterde J (2017). Curvature approximation from parabolic sectors. Image Anal Stereol 36(3):233--41.
Gundersen HJG, Jensen EB (1987). The efficiency of systematic sampling in stereology and its prediction. J Microsc-Oxford 147:229--63.
Howard V, Reed MG (2005) Unbiased Stereology, 2nd ed.; Garland science/BIOS Scientific Publishers, Oxford: England.
Sadafi A, Bordukova M, Makhro A, Navab N, Bogdanova A, Marr C (2023). RedTell: an AI tool for interpretable analysis of red blood cell morphology. Front Physiol 14:1058720.
Wheeless LL, Robinson RD, Lapets OP, Cox C, Rubio A, Weintraub, M, Benjamin, LJ (1994). Classification of Red Blood Cells as Normal, Sickle, or Other Abnormal, Using a Single Image Analysis Feature. Cytometry 17:159-66.
Young IT, Walker JE, Bowie JE (1974). An Analysis Technique for Biological Shape. Inform Control 25:357--70.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Lluisa Gual-Vaya
This work is licensed under a Creative Commons Attribution 4.0 International License.
