Radial textures: a new approach to analyze meat quality by using MRI
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Radial textures : a new approach to analyze meat quality by using MRI. / Caballero, Daniel; Caro, Andres; Amigo, Jose Manuel; Ávila, Mar; Antequera, Teresa; Pérez-Palacios, Trinidad.
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 23rd Iberoamerican Congress, CIARP 2018, Proceedings: 23rd Iberoamerican Congress, CIARP 2018Madrid, Spain, November 19–22, 2018. ed. / Ruben Vera-Rodriguez; Julian Fierrez; Aythami Morales. Springer, 2019. p. 479-486 (Lecture notes in computer science, Vol. 11401).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Radial textures
T2 - 23rd Iberoamerican Congress, CIARP 2018
AU - Caballero, Daniel
AU - Caro, Andres
AU - Amigo, Jose Manuel
AU - Ávila, Mar
AU - Antequera, Teresa
AU - Pérez-Palacios, Trinidad
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Algorithms
KW - Iberian loin
KW - MRI
KW - Quality traits
KW - Texture
U2 - 10.1007/978-3-030-13469-3_56
DO - 10.1007/978-3-030-13469-3_56
M3 - Article in proceedings
AN - SCOPUS:85063036180
SN - 978-3-030-13468-6
T3 - Lecture notes in computer science
SP - 479
EP - 486
BT - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 23rd Iberoamerican Congress, CIARP 2018, Proceedings
A2 - Vera-Rodriguez, Ruben
A2 - Fierrez, Julian
A2 - Morales, Aythami
PB - Springer
Y2 - 19 November 2018 through 22 November 2018
ER -
ID: 216303189