Comparison of different image analysis algorithms on MRI to predict physico-chemical and sensory attributes of loin

Research output: Contribution to journalJournal article – Annual report year: 2018Researchpeer-review

View graph of relations

Computer vision algorithms on MRI have been presented as an alternative to destructive methods to determine the quality traits of meat products. Since, MRI is non-destructive, non-ionizing and innocuous methods. 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 (Classical Fractal Algorithm –CFA-, Fractal Texture Algorithm –FTA- and OPFTA) and three classical texture algorithms (Grey level co-occurrence matrix –GLCM-, Grey level run length matrix –GLRLM- and
Neighbouring grey level dependence matrix –NGLDM-) were tested in this study. The results obtained by means of these computer vision algorithms were correlated to the results obtained by means of physico-chemical and sensory analysis. CFA reached low relationship for the quality parameters of loins, the remaining algorithms achieved correlation coefficients higher than 0.5 noting OPFTA that reached the highest correlation coefficients in all cases except for the L* coordinate color that GLCM obtained the highest correlation coefficient. These high correlation coefficients confirm the new algorithm as an alternative to the other computer vision approaches in order to compute the physico chemical and sensory parameters of meat products in a non-destructive and efficient way.
Original languageEnglish
JournalChemometrics and intelligent laboratory systems
Pages (from-to)54-63
Number of pages10
Publication statusPublished - 2018
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Fractal, Texture features, Algorithms, Data mining, Food technology
Download as:
Download as PDF
Select render style:
Download as HTML
Select render style:
Download as Word
Select render style:

ID: 146511053