Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-10T10:24:10.208Z Has data issue: false hasContentIssue false
  • English
  • Français

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: cfontes@ufba.br
Get access Rights & Permissions [Opens in a new window]

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bai, J, Xue, H and Zhou, Y (2015) The milk somatic cell image segmentation method based on dimension reduction and fusion. In Li, D and Li, Z (eds) Computer and Computing Technologies in Agriculture IX. CCTA 2015. IFIP Advances in Information and Communication Technology. Cham: Springer, p. 478.Google Scholar
Beleites, C, Neugebauer, U, Bocklitz, T, Krafft, C and Popp, J (2013) Sample size planning for classification models. Analytica Chimica Acta 760, 25–33.CrossRefGoogle ScholarPubMed
Chaloupková, V, Ivanova, T, Ekrt, O, Kabutey, A and Herák, D (2018) Determination of particle size and distribution through image-based macroscopic analysis of the structure of biomass briquettes. Energies 11, 331.CrossRefGoogle Scholar
Das K, R and Imon, AHMR (2016) A brief review of tests for normality. American Journal of Theoretical and Applied Statistics 5, 5–12.Google Scholar
Gao, X, Xue, H, Pan, X, Jiang, X, Zhou, Y and Luo, X (2017) Somatic cells recognition by application of Gabor. International Journal of Pattern Recognition and Artificial Intelligence 31, 1757009.CrossRefGoogle Scholar
Khan, MF and Khan, MA (2018) Optik information preserving histogram segmentation of low contrast images using fuzzy measures. Optik – International Journal for Light and Electron Optics 157, 1397–1404.Google Scholar
Melo, GJA, Gomes, V, Baccili, CC, Almeida, LAL and Lima, AC (2015) A robust segmentation method for counting bovine milk somatic cells in microscope slide images. Computers and Electronics in Agriculture 115, 142–149.CrossRefGoogle Scholar
Prescott, SC and Breed, RS (1910) The determination of the number of body cells. American Journal of Public Hygiene 20, 663–664.Google ScholarPubMed
Ramaraj, M and Niraimathi, S (2017) Application of color based image segmentation paradigm on rgb color pixels using fuzzy c-means and k means algorithms. International Journal of Computer Science and Mobile Computing 6, 430–440.Google Scholar
Schober, P, Boer, C and Schwarte, LA (2018) Correlation coefficients: appropriate use and interpretation. Anaesthesia and Analgesia 126, 1763–1768.CrossRefGoogle ScholarPubMed
Xu, R, Xu, L and Xu, B (2017) Assessing CO2 emissions in China's iron and steel industry: evidence from quantile regression approach. Journal of Cleaner Production 152, 259–270.CrossRefGoogle Scholar
Xue, H, Li, H, Wang, Y and Zhao, T (2009) The segmentation of color milk somatic cells images. 2nd International Congress on Image and Signal Processing, Tianjin, pp. 1–4.CrossRefGoogle Scholar
Supplementary material: PDF

Silva Ramos et al. supplementary material

Silva Ramos et al. supplementary material

Download Silva Ramos et al. supplementary material(PDF)
PDF 467.2 KB