Automatic segmentation of beef longissimus dorsi muscle and marbling by an adaptable algorithm

Correlation of the Attributes Measured by Computer Vision with Moisture and Fat Content of Meat Batters

“…A plate filled with a batter was placed into a matte blue background (Jackman et al, 2009) on the photographic bench. The images were taken with digital camera (Nikon D90, Nikon Corporation, Tokyo, Japan) located vertically at a distance of 30 cm from the sample.…”

Optical Methods and Techniques for Meat Quality Inspection

“…Examples include prediction of shrinkage of ellipsoid beef joints (Zheng et al, 2007); prediction of color, marbling, and surface texture ; measurement of texture features to classify beef as tough or tender (Li et al, 2001); prediction of skeletal maturity based on cartilage ossification in the thoracic vertebrae (Hatem et al, 2003); measurement of CIE L*, a*, b*, hue angle, and chroma (Larrain et al, 2008); automatic segmentation of the longissimus dorsi muscle and marbling (Jackman et al, 2009); prediction of percentage surface metmyoglobin on fresh beef, which causes wide variation in surface color (Demos et al, 1996); and classification of fresh and stained meat samples based on marbling in the longissimus thoracis muscle (Pena et al, 2013).…”

Prediction of beef palatability from colour, marbling and surface texture features of longissimus dorsi

“…The normal view image required segmentation into longissimus dorsi (LD) muscle and marbling. An automatic segmentation algorithm was used (Jackman et al, 2009b) to split the images into LD muscle, marbling and background. The image backgrounds were eliminated by thresholding in the hue colour channel (Data in the HSI colourspace is in normalised double precision form in a range 0-1 for each channel).…”