Development of a New Fractal Algorithm to Predict Quality Traits of MRI Loins

  • Daniel CaballeroEmail author
  • Andrés Caro
  • José Manuel Amigo
  • Anders B. Dahl
  • Bjarne K. Ersbøll
  • Trinidad Pérez-Palacios
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10424)


Traditionally, the quality traits of meat products have been estimated by means of physico-chemical methods. Computer vision algorithms on MRI have also been presented as an alternative to these destructive methods since MRI is non-destructive, non-ionizing and innocuous. The use of fractals to analyze MRI could be another possibility for this purpose. In this paper, a new fractal algorithm is developed, to obtain features from MRI based on fractal characteristics. This algorithm is called OPFTA (One Point Fractal Texture Algorithm). Three fractal algorithms were tested in this study: CFA (Classical fractal algorithm), FTA (Fractal texture algorithm) and OPFTA. The results obtained by means of these three fractal algorithms were correlated to the results obtained by means of physico-chemical methods. OPFTA and FTA achieved correlation coefficients higher than 0.75 and CFA reached low relationship for the quality parameters of loins. The best results were achieved for OPFTA as fractal algorithm (0.837 for lipid content, 0.909 for salt content and 0.911 for moisture). These high correlation coefficients confirm the new algorithm as an alternative to the classical computational approaches (texture algorithms) in order to compute the quality parameters of meat products in a non-destructive and efficient way.


MRI Fractal Algorithms Quality traits Iberian loin 



The authors wish to acknowledge the funding received from the FEDER-MICCIN Infrastructure Research Project (UNEX-10-1E-402), Junta de Extremadura economic support for research group (GRU15173 and GRU15113) and the COST association, Farm Animal Imaging action (FAIM) (COST-FA1102) (COST-STSM-FA1102-26642). We also wish to thank the Animal Source Foodstuffs Innovation Service (SiPA, Cáceres, Spain) from the University of Extremadura.


  1. 1.
    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
  2. 2.
    Fantazzini, P., Gombia, M., Schembri, P., Simoncini, N., Virgili, R.: Use of magnetic Resonance Imaging for monitoring Parma dry-cured ham processing. Meat Sci. 82, 219–227 (2009)CrossRefGoogle Scholar
  3. 3.
    Manzoco, L., Anese, M., Marzona, S., Innocente, N., Lazaglio, C., Nicoli, M.C.: Monitoring dry-curing of San Daniele ham by magnetic resonance imaging. Food Chem. 141, 2246–2252 (2013)CrossRefGoogle Scholar
  4. 4.
    Cernadas, E., Antequera, T., Rodríguez, P.G., Durán, M.L., Gallardo, R., Villa, D.: Magnetic resonance imaging to classify loin from Iberian pig. In: Webb, G.A., Belton, P.S., Gil, A.M., Delgadillo, I. (Eds.) Magnetic Resonance Imaging in Food Science: A View to the Future. The Royal Society of Chemistry. Cambridge (2001)Google Scholar
  5. 5.
    Cernadas, E., Carrión, P., Rodríguez, P.G., Muriel, E., Antequera, T.: Analyzing magnetic resonance images of Iberian pork loin to predict its sensorial characteristics. Comput. Vis. Image Underst. 98, 345–361 (2005)CrossRefGoogle Scholar
  6. 6.
    Ávila, M.M., Durán, M.L., Antequera, T., Palacios, R., Luquero, M.: 3D reconstruction on mri to analyse marbling and fat level in iberian loin. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, vol. 4477, pp. 145–152. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-72847-4_20 CrossRefGoogle Scholar
  7. 7.
    Pérez-Palacios, T., Caballero, D., Caro, A., Rodríguez, P.G., Antequera, T.: Applying data mining and computer vision techniques to MRI to estimate quality traits in Iberian hams. J. Food Eng. 131, 82–88 (2014)CrossRefGoogle Scholar
  8. 8.
    Pérez-Palacios, T., Caballero, D., Caro, A., Antequera, T.: Magnetic resonance imaging and computational texture features to predict moisture and lipid content of loins. In: IV Farm Animal Imaging Conference, Edinburgh, UK (2015)Google Scholar
  9. 9.
    Jackman, P., Sun, D.W., Allen, P.: 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
  10. 10.
    Jackman, P., Sun, D.W.: Recent advances in image processing using image texture features for food quality assessment. Trends Food Sci. Technol. 29(1), 35–43 (2013)CrossRefGoogle Scholar
  11. 11.
    Celigueta-Torres, I., Amigo-Rubio, J.M., Ipsen, R.: Using fractal image analysis to characterize microstructure of low-fat stirred yogurt manufactured with microparticulated whey protein. J. Food Eng. 109, 721–729 (2012)CrossRefGoogle Scholar
  12. 12.
    Sun, J., Zhang, Y.B., Dahl, A.B., Conradsen, K., Juul Jensen, D.: Boundary fractal analysis of two cube-oriented grains in partly recrystallized copper. In: XVII International Conference on Texture of Materials, ICOTOM 2017, Dresden, Germany (2014)Google Scholar
  13. 13.
    Quevedo, R., Pedreschi, F., Bastías, J.M., Díaz, O.: Correlation of the fractal enzymatic browning rate with the temperature in mushroom, pear and apple slices. LWT-Food Sci. Technol. 65, 406–413 (2016)CrossRefGoogle Scholar
  14. 14.
    Manera, M., Giari, L., De Pasquale, J.A., Dezfuli, B.S.: Local connected fractal dimmension analysis in gill of fish experimentally exposed to toxicants. Aquat. Toxicol. 175, 12–19 (2016)CrossRefGoogle Scholar
  15. 15.
    Zapotoczny, P., Szczypinski, P.M., Daszkiewicz, T.: Evaluation of the quality of cold meats by computer-assisted image analysis. LWT-Food Sci. Technol. 67, 37–49 (2016)CrossRefGoogle Scholar
  16. 16.
    Tsuta, M., Sugiyama, J., Sagara, Y.: Near-infrared imaging spectroscopy based on sugar absorption band for melons. J. Agric. Food Chem. 50(1), 48–52 (2002)CrossRefGoogle Scholar
  17. 17.
    Polder, G., Van Der Heijden, G.W.A.M., Van Der Hoet, H., Young, I.T.: Measuring surface distribution of caretones and chlorophyll in ripening tomatoes using imaging spectrometry. Postharvest Biol. Technol. 34, 117–129 (2004)CrossRefGoogle Scholar
  18. 18.
    Association of Official Analytical Chemist (AOAC): Official Methods of Analysis of AOAC International, 17th edn. AOAC International. Gaithersburg, Maryland, U.S.AGoogle Scholar
  19. 19.
    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
  20. 20.
    Mandelbrot, B.B.: The Fractal Geometry of Nature. W.H. Freeman and Co., New York (1982)zbMATHGoogle Scholar
  21. 21.
    Caballero, D., Caro, A., Antequera, T., Pérez-Palacios, T.: Non destructive analysis of loin by magnetic resonance imaging and fractal. In: IX Sympossium of Mediterranean Pig, Portalegre, Portugal (2016)Google Scholar
  22. 22.
    Peckinpaugh, S.: An improved method for computing gray-level coocurrence matrix based texture measured. Comput. Vis. Graph. Image Process. 53, 574–580 (1991)Google Scholar
  23. 23.
    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
  24. 24.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan-Kauffmann, San Francisco (2005)zbMATHGoogle Scholar
  25. 25.
    Kira, K., Rendell, L.A.: A practical approach to feature selection. In: IX International Conference on Machine Learning, Aberdeen, UK (1992)Google Scholar
  26. 26.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Texture features for image classification. IEEE Trans. Man Cybern. 3(6), 610–621 (1973)CrossRefGoogle Scholar
  27. 27.
    Sun, C., Wee, G.: Neighboring gray level dependence matrix. Comput. Vis. graph. Image Proc. 23, 341–352 (1982)CrossRefGoogle Scholar
  28. 28.
    Siew, L.H., Hodgson, R.M., Wood, E.J.: Texture measures for carpet wear assessment. IEEE Trans. Pattern Anal. Mach. Intell. 10(1), 92–104 (1988)CrossRefGoogle Scholar
  29. 29.
    Durán, M.L., Rodríguez, P.G., Arias-Nicolas, J.P., Martín, J., Disdier, C.: A perceptual similarity method by pairwise comparison in a medical image case. Mach. Vis. Appl. 21(6), 865–877 (2010)CrossRefGoogle Scholar
  30. 30.
    Colton, T.: Statistics in Medicine. Little Brown and Co., New York (1974)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Daniel Caballero
    • 1
    Email author
  • Andrés Caro
    • 1
  • José Manuel Amigo
    • 2
  • Anders B. Dahl
    • 3
  • Bjarne K. Ersbøll
    • 4
  • Trinidad Pérez-Palacios
    • 5
  1. 1.Computer Science Department, Research Institute of Meat and Meat ProductUniversity of ExtremaduraCáceresSpain
  2. 2.Department of Food Science, Quality and Technology, Faculty of Life ScienceUniversity of CopenhagenFrediksberg CDenmark
  3. 3.Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKongens LyngbyDenmark
  4. 4.Department of Informatics and Mathematical ModellingTechnical University of DenmarkKongens LyngbyDenmark
  5. 5.Food Technology Department, Research Institute of Meat and Meat ProductUniversity of ExtremaduraCáceresSpain

Personalised recommendations