Beef quality parameters estimation using ultrasound and color images

• Published in: BMC bioinformatics Vol. 16; no. Suppl 4; p. S6
• Main Authors: Nunes, Jose Luis, Piquerez, Martín, Pujadas, Leonardo, Armstrong, Eileen, Fernández, Alicia, Lecumberry, Federico
• Format: Journal Article
• Language: English
• Published: London BioMed Central 23.02.2015
• Subjects: Adipose Tissue - diagnostic imaging; Algorithms; Animals; Bioinformatics; Biomedical and Life Sciences; Cattle; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Databases, Factual; Food Quality; Life Sciences; Meat - analysis; Meat - standards; Microarrays; Models, Statistical; Muscle, Skeletal - diagnostic imaging; Regression Analysis; Ribs - diagnostic imaging; Ultrasonography - methods; support vector regression; curve evolution; beef quality; intramuscular fat; feature extraction; ribeye; backfat; ultrasound images
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-16-S4-S6

Background Beef quality measurement is a complex task with high economic impact. There is high interest in obtaining an automatic quality parameters estimation in live cattle or post mortem. In this paper we set out to obtain beef quality estimates from the analysis of ultrasound (in vivo) and color images (post mortem), with the measurement of various parameters related to tenderness and amount of meat: rib eye area, percentage of intramuscular fat and backfat thickness or subcutaneous fat. Proposal An algorithm based on curve evolution is implemented to calculate the rib eye area. The backfat thickness is estimated from the profile of distances between two curves that limit the steak and the rib eye, previously detected. A model base in Support Vector Regression (SVR) is trained to estimate the intramuscular fat percentage. A series of features extracted on a region of interest, previously detected in both ultrasound and color images, were proposed. In all cases, a complete evaluation was performed with different databases including: color and ultrasound images acquired by a beef industry expert, intramuscular fat estimation obtained by an expert using a commercial software, and chemical analysis. Conclusions The proposed algorithms show good results to calculate the rib eye area and the backfat thickness measure and profile. They are also promising in predicting the percentage of intramuscular fat.