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Somatic cell count in buffalo milk using fuzzy clustering and image processing techniques

Published online by Cambridge University Press:  17 February 2021

Aline Silva Ramos ,
Cristiano Hora Fontes ,
Adonias Magdiel Ferreira ,
Camila Costa Baccili ,
Karen Nascimento da Silva ,
Viviani Gomes  and
Gabriel Jesus Alves de Melo
Aline Silva Ramos
Affiliation:
Graduate Program in Industrial Engineering, Polytechnic Institute, Federal University of Bahia, Salvador, Brazil
Cristiano Hora Fontes*
Affiliation:
Graduate Program in Industrial Engineering, Polytechnic Institute, Federal University of Bahia, Salvador, Brazil
Adonias Magdiel Ferreira
Affiliation:
Graduate Program in Industrial Engineering, Polytechnic Institute, Federal University of Bahia, Salvador, Brazil
Camila Costa Baccili
Affiliation:
College of Veterinary Medicine and Animal Science, University of São Paulo, São Paulo, Brazil
Karen Nascimento da Silva
Affiliation:
College of Veterinary Medicine and Animal Science, University of São Paulo, São Paulo, Brazil
Viviani Gomes
Affiliation:
College of Veterinary Medicine and Animal Science, University of São Paulo, São Paulo, Brazil
Gabriel Jesus Alves de Melo
Affiliation:
Federal Institute of Bahia, Ilhéus, Brazil
*
Author for correspondence: Cristiano Hora Fontes, Email: [email protected]
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Abstract

This research communication presents an automatic method for the counting of somatic cells in buffalo milk, which includes the application of a fuzzy clustering method and image processing techniques (somatic cell count with fuzzy clustering and image processing|, SCCFCI). Somatic cell count (SCC) in milk is the main biomarker for assessing milk quality and it is traditionally performed by exhaustive methods consisting of the visual observation of cells in milk smears through a microscope, which generates uncertainties associated with human interpretation. Unlike other similar works, the proposed method applies the Fuzzy C-Means (FCM) method as a preprocessing step in order to separate the images (objects) of the cells into clusters according to the color intensity. This contributes signficantly to the performance of the subsequent processing steps (thresholding, segmentation and recognition/identification). Two methods of thresholding were evaluated and the Watershed Transform was used for the identification and separation of nearby cells. A detailed statistical analysis of the results showed that the SCCFCI method is able to provide results which are consistent with those obtained by conventional counting. This method therefore represents a viable alternative for quality control in buffalo milk production.

Keywords

Biomarkerfuzzy clusteringimage processingmastitismilk quality
Type
Research Article
Information
Journal of Dairy Research , Volume 88 , Issue 1 , February 2021 , pp. 69 - 72
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of Hannah Dairy Research Foundation

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