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

  • Daniel Caballero
  • 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)

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.

Keywords

MRI Fractal Algorithms Quality traits Iberian loin 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Daniel Caballero
    • 1
  • 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

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