Radial Textures: A New Approach to Analyze Meat Quality by Using MRI

  • Daniel CaballeroEmail author
  • Andrés Caro
  • José Manuel Amigo
  • Mar Ávila
  • Teresa Antequera
  • Trinidad Pérez-Palacios
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)


Traditionally, the quality traits of meat products have been determined by means of physico-chemical methods. As an alternative, computer vision algorithms applied on MRI have been proposed, mainly, because of the non-destructive, non-ionizing and innocuous nature of MRI. Usually, the computer vision algorithms developed to analyze meat quality are based in classical textures. In this paper, a new texture algorithm (called RTA, Radial Texture Algorithm) based on the radial distribution of the images and second order statistics is proposed. The results obtained by RTA were compared to the obtained by means of three well known classical texture algorithms: GLCM (Gray Level Co-occurrence Matrix), GLRLM (Gray Level Run Length Matrix) and NGLDM (Neighbouring Gray Level Dependence Matrix) and correlated to the results obtained by means of physico-chemical methods. GLRLM and NGLDM achieved correlation coefficients between 0.50 and 0.75 whereas RTA and GLCM reached very good to excellent relationship (R > 0.75) for the quality parameters of loins. RTA achieved the best results (0.988 for moisture, 0.883 for lipid content and 0.992 for salt content). These high correlation coefficients confirm the new algorithm as a firm alternative to the classical computational approaches in order to compute the quality traits of meat products in a non-destructive and efficient way.


MRI Algorithms Texture Quality traits Iberian loin 


  1. 1.
    Mahendran, R., Jayashree, G.C., Alagusundaram, K.: Application of computer vision techniques on sorting and grading of fruits and vegetables. J. Food Process. Technol. S1-001 10, 2157–7110 (2012)Google Scholar
  2. 2.
    Brosnan, T., Sun, D.W.: Improving quality inspection of food products by computer vision - a review. J. Food Eng. 61, 3–16 (2004)CrossRefGoogle Scholar
  3. 3.
    Caballero, D., et al.: Comparison of different image analysis algorithms on MRI to predict physico-chemical and sensory attributes of loin. Chemometr. Intell. Lab. Syst. 180, 54–63 (2018)CrossRefGoogle Scholar
  4. 4.
    Antequera, T., Caro, A., Rodríguez, P.G., Pérez-Palacios, T.: Monitoring the ripening process of Iberian ham by Computer Vision on Magnetic Resonance Imaging. Meat Sci. 76, 561–567 (2007)CrossRefGoogle Scholar
  5. 5.
    Fantazzini, P., Gombia, M., Schembri, M., Simoncini, N., Virgili, R.: Use of Magnetic Resonance Imaging for monitoring Parma dry-cured ham processing. Meat Sci. 82, 219–227 (2009)CrossRefGoogle Scholar
  6. 6.
    Manzoco, L., Anese, M., Marzona, S., Innocente, N., Lazagio, C., Nicoli, M.C.: Monitoring dry-curing of San Daniele ham by Magnetic Resonance Imaging. Food Chem. 141, 2246–2252 (2013)CrossRefGoogle Scholar
  7. 7.
    Caballero, D., et al.: Modeling salt diffusion in Iberian ham by applying MRI and data mining. J. Food Eng. 189, 115–122 (2016)CrossRefGoogle Scholar
  8. 8.
    Pérez-Palacios, T., Caballero, D., Antequera, T., Durán, M.L., Ávila, M.M., Caro, A.: Optimization of MRI acquisition and texture analysis to predict physico-chemical parameters of loins by data mining. Food Bioprocess Technol. 10, 750–758 (2017)CrossRefGoogle Scholar
  9. 9.
    Shiramita, K., Miyajima, T., Takiyama, R.: Determination of meat quality by texture analysis. Pattern Recogn. Lett. 19, 1319–1324 (1998)CrossRefGoogle Scholar
  10. 10.
    Li, J., Tan, J., Martz, F.A., Heymann, H.: Image texture features as indicators of beef tenderness. Meat Sci. 53, 17–22 (1999)CrossRefGoogle Scholar
  11. 11.
    Jackman, P., Sun, D.W.: Recent advances in the use of computer vision technology in the quality assessment of fresh meat. Trends Food Sci. Technol. 22(4), 185–197 (2011)CrossRefGoogle Scholar
  12. 12.
    Jackman, P., Sun, D.W.: Recent advances in image processing using image texture features for food quality assessment. Trends Food Sci. Technol. 19, 35–43 (2013)CrossRefGoogle Scholar
  13. 13.
    Association of Official Analytical Chemists (AOAC): Official method of analysis of AOAC international. 17th edn. AOAC International. Gaithersburg, Maryland, USAGoogle Scholar
  14. 14.
    Pérez-Palacios, T., Ruiz, J., Martín, D., Muriel, E., Antequera, T.: Comparison of different methods for total lipid quantification. Food Chem. 110, 1025–1029 (2008)CrossRefGoogle Scholar
  15. 15.
    Molano, R., Rodríguez, P.G., Caro, A., Durán, M.L.: Finding the largest area rectangle of arbitrary orientation in a closed contour. Appl. Math. Comput. 218(19), 9866–9874 (2012)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Man Cybern. 3(6), 610–621 (1973)CrossRefGoogle Scholar
  17. 17.
    Galloway, M.M.: Texture classification using gray level dependence matrix. Comput. Vis. Image Process. 4, 172–179 (1975)CrossRefGoogle Scholar
  18. 18.
    Sun, C., Wee, G.: Neighbouring gray level dependence matrix. Comput. Vis. Image Process. 23, 341–352 (1982)CrossRefGoogle Scholar
  19. 19.
    Peckinpaugh, S.: An improved method for computing gray-level co-occurrence matrix based texture measured. Comput. Vis. Graph. Image Process. 53, 574–580 (1991)Google Scholar
  20. 20.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan-Kauffmann, San Francisco (2005)zbMATHGoogle Scholar
  21. 21.
    Colton, T.: Statistics in Medicine. Little Brown and Co., New York (1974)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Science Department, Research Institute of Meat and Meat ProductUniversity of ExtremaduraCáceresSpain
  2. 2.Chemometrics and Analytical Technology, Department of Food ScienceUniversity of CopenhagenFrederiksberg CDenmark
  3. 3.Food Technology Department, Research Institute of Meat and Meat ProductUniversity of ExtremaduraCáceresSpain

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