Literature DB >> 20416763

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

Patrick Jackman1, Da-Wen Sun, Paul Allen.   

Abstract

An algorithm for automatic segmentation of beef longissimus dorsi (LD) muscle and marbling has been developed. The algorithm used simple thresholding to remove the background and then used clustering and thresholding with contrast enhancement via a customised greyscale to remove marbling. It was possible to attain lean muscle free of obvious marbling or background pixels where specular reflection could be effectively mitigated. Features of the automatically derived LD muscle and marbling images were compared to corresponding features of LD muscle and marbling images derived with a segmentation method requiring manual completion. Very strong correlations (up to r=1) were found between the colour features of both sets of LD muscle images. Strong correlations (up to r=0.96) were found between the features of both sets of marbling images. The automatic segmentation method has shown its good ability to approximate colour and marbling features. The algorithm has adaptable parameters and can be retailored to suit different image acquisition environments.

Year:  2009        PMID: 20416763     DOI: 10.1016/j.meatsci.2009.03.010

Source DB:  PubMed          Journal:  Meat Sci        ISSN: 0309-1740            Impact factor:   5.209


  3 in total

1.  An investigation on the relationship among marbling features, physiological age and Warner-Bratzler Shear force of steer longissimus dorsi muscle.

Authors:  Lingying Luo; Dandan Guo; Guanghong Zhou; Kunjie Chen
Journal:  J Food Sci Technol       Date:  2018-01-27       Impact factor: 2.701

2.  Beef quality parameters estimation using ultrasound and color images.

Authors:  Jose Nunes; Martín Piquerez; Leonardo Pujadas; Eileen Armstrong; Alicia Fernández; Federico Lecumberry
Journal:  BMC Bioinformatics       Date:  2015-02-23       Impact factor: 3.169

3.  High Throughput Multispectral Image Processing with Applications in Food Science.

Authors:  Panagiotis Tsakanikas; Dimitris Pavlidis; George-John Nychas
Journal:  PLoS One       Date:  2015-10-14       Impact factor: 3.240

  3 in total

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