Learn More
In this study, computed tomography (CT) technology was used to measure body composition on live pigs for breeding purposes. Norwegian Landrace (L; n = 3835) and Duroc (D; n = 3139) boars, selection candidates to be elite boars in a breeding programme, were CT-scanned between August 2008 and August 2010 as part of an ongoing testing programme at Norsvin's(More)
The aim of this study was to develop a method for scoring osteochondrosis (OC) by using information from computed tomography (CT), as well as to estimate the heritability for OC scored by means of CT (OCwCT) of the medial and lateral condyles at the distal end of the humerus or the femur of the right and left leg and the sum of these scores (OCT). In(More)
The ability to accurately measure body or carcass composition is important for performance testing, grading and finally selection or payment of meat-producing animals. Advances especially in non-invasive techniques are mainly based on the development of electronic and computer-driven methods in order to provide objective phenotypic data. The preference for(More)
One hundred and four pure-bred Norwegian Duroc boars were CT (computed tomography) scanned to predict the in vivo intramuscular fat percentage in the loin. The animals were slaughtered and the loin was cut commercially. A muscle sample of the m. Longissimus dorsi was sampled and analyzed by the use of near-infra-tested to improve predictions. The results(More)
BACKGROUND A significant heritability has been documented for articular osteochondrosis. Selection against osteochondrosis has historically been based on macroscopic evaluation, but as computed tomography (CT) now is used to select boars with optimal body composition it can potentially also be used to screen for osteochondrosis. False negative diagnosis(More)
The grading of farmed animal carcasses depends on the content of lean meat, fat and bone. Current imaging technologies are able to detect and represent carcass composition in images. To extract information from these images, specialized image processing techniques are required. In this paper, we propose a new segmentation method to accurately separate lean(More)
  • 1