Radial textures: a new approach to analyze meat quality by using MRI

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Standard

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 proceedingArticle in proceedingsResearchpeer-review

Harvard

Caballero, D, Caro, A, Amigo, JM, Ávila, M, Antequera, T & Pérez-Palacios, T 2019, Radial textures: a new approach to analyze meat quality by using MRI. in R Vera-Rodriguez, J Fierrez & A Morales (eds), 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. Springer, Lecture notes in computer science, vol. 11401, pp. 479-486, 23rd Iberoamerican Congress, CIARP 2018, Maddrid, Spain, 19/11/2018. https://doi.org/10.1007/978-3-030-13469-3_56

APA

Caballero, D., Caro, A., Amigo, J. M., Ávila, M., Antequera, T., & Pérez-Palacios, T. (2019). Radial textures: a new approach to analyze meat quality by using MRI. In R. Vera-Rodriguez, J. Fierrez, & A. Morales (Eds.), 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 (pp. 479-486). Springer. Lecture notes in computer science, Vol.. 11401 https://doi.org/10.1007/978-3-030-13469-3_56

Vancouver

Caballero D, Caro A, Amigo JM, Ávila M, Antequera T, Pérez-Palacios T. Radial textures: a new approach to analyze meat quality by using MRI. In Vera-Rodriguez R, Fierrez J, Morales A, editors, 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. Springer. 2019. p. 479-486. (Lecture notes in computer science, Vol. 11401). https://doi.org/10.1007/978-3-030-13469-3_56

Author

Caballero, Daniel ; Caro, Andres ; Amigo, Jose Manuel ; Ávila, Mar ; Antequera, Teresa ; Pérez-Palacios, Trinidad. / Radial textures : a new approach to analyze meat quality by using MRI. 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. editor / Ruben Vera-Rodriguez ; Julian Fierrez ; Aythami Morales. Springer, 2019. pp. 479-486 (Lecture notes in computer science, Vol. 11401).

Bibtex

@inproceedings{ce19e6d44bce4a7aa3ee04c0e6b7189c,
title = "Radial textures: a new approach to analyze meat quality by using MRI",
abstract = "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.",
keywords = "Algorithms, Iberian loin, MRI, Quality traits, Texture",
author = "Daniel Caballero and Andres Caro and Amigo, {Jose Manuel} and Mar {\'A}vila and Teresa Antequera and Trinidad P{\'e}rez-Palacios",
year = "2019",
doi = "10.1007/978-3-030-13469-3_56",
language = "English",
isbn = "978-3-030-13468-6",
pages = "479--486",
editor = "Ruben Vera-Rodriguez and Julian Fierrez and Aythami Morales",
booktitle = "Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 23rd Iberoamerican Congress, CIARP 2018, Proceedings",
publisher = "Springer",

}

RIS

TY - GEN

T1 - Radial textures

T2 - a new approach to analyze meat quality by using MRI

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

SN - 978-3-030-13468-6

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

ER -

ID: 216303189