Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Computer Analysis of Images and Patterns (CAIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10424))

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  18. Association of Official Analytical Chemist (AOAC): Official Methods of Analysis of AOAC International, 17th edn. AOAC International. Gaithersburg, Maryland, U.S.A

    Google Scholar 

  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)

    Article  Google Scholar 

  20. Mandelbrot, B.B.: The Fractal Geometry of Nature. W.H. Freeman and Co., New York (1982)

    MATH  Google Scholar 

  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. 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. 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)

    MathSciNet  MATH  Google Scholar 

  24. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan-Kauffmann, San Francisco (2005)

    MATH  Google Scholar 

  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. Haralick, R.M., Shanmugam, K., Dinstein, I.: Texture features for image classification. IEEE Trans. Man Cybern. 3(6), 610–621 (1973)

    Article  Google Scholar 

  27. Sun, C., Wee, G.: Neighboring gray level dependence matrix. Comput. Vis. graph. Image Proc. 23, 341–352 (1982)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  30. Colton, T.: Statistics in Medicine. Little Brown and Co., New York (1974)

    Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

  1. Computer Science Department, Research Institute of Meat and Meat Product, University of Extremadura, Av/ Universidad S/N, 10003, Cáceres, Spain

    Daniel Caballero & Andrés Caro

  2. Department of Food Science, Quality and Technology, Faculty of Life Science, University of Copenhagen, Rolighedsvej 30, 1958, Frediksberg C, Denmark

    José Manuel Amigo

  3. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersen Plads, Building 324, 2800, Kongens Lyngby, Denmark

    Anders B. Dahl

  4. Department of Informatics and Mathematical Modelling, Technical University of Denmark, Richard Petersen Plads, Building 324, 2800, Kongens Lyngby, Denmark

    Bjarne K. Ersbøll

  5. Food Technology Department, Research Institute of Meat and Meat Product, University of Extremadura, Av/ Universidad S/N, 10003, Cáceres, Spain

    Trinidad Pérez-Palacios

Authors
  1. Daniel Caballero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrés Caro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. José Manuel Amigo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anders B. Dahl
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bjarne K. Ersbøll
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Trinidad Pérez-Palacios
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel Caballero .

Editor information

Editors and Affiliations

  1. Linköping University, Linköping, Sweden

    Michael Felsberg

  2. Lund University, Lund, Sweden

    Anders Heyden

  3. University of Southern Denmark, Odense, Denmark

    Norbert Krüger

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Caballero, D., Caro, A., Amigo, J.M., Dahl, A.B., Ersbøll, B.K., Pérez-Palacios, T. (2017). Development of a New Fractal Algorithm to Predict Quality Traits of MRI Loins. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10424. Springer, Cham. https://doi.org/10.1007/978-3-319-64689-3_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-64689-3_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64688-6

  • Online ISBN: 978-3-319-64689-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation