1 Image Analysis and Computer Graphics, Department of Informatics and Mathematical Modeling, Technical University of Denmark2 Department of Informatics and Mathematical Modeling, Technical University of Denmark3 DTU Data Analysis, Department of Informatics and Mathematical Modeling, Technical University of Denmark4 Department of Applied Mathematics and Computer Science, Technical University of Denmark
The Danish pig meat industry has been seeing a growing international competition in the past years. In the quest to maintain both competitive prices and high product standards in spite of the higher Danish factor costs, a substantial effort is being put into innovation, research and development of technology. Recently, the use of X-ray computed tomography (CT) coupled with methods from image analysis has been introduced as a powerful means to optimise production, by providing detailed information on the raw materials. This thesis covers two aspects of the application of CT in the modern abattoir. In the first aspect we use CT to analyse the biological diversity of carcass populations. The images form the basis for a data-driven tissue deformation model. The results provide valuable input to assist the development of an automated robotic tool for trimming the rind off pig backs. The second aspect concerns measurements of each single carcass, to improve the raw material utilisation by individually adapted processing. Measurements performed online in the abattoir demand fast, robust and cost-effective imaging. We propose a tomographic reconstruction algorithm, enabling a substantial reduction of the subject-specific X-ray data needed to produce high quality images for accurate measurements. This is very beneficial for the abattoirs, as a reduction in acquired data translates directly into higher speed and a lower cost. The thesis demonstrates the great potential of CT as a technology for improving the yield of the Danish pig meat industry. An introduction of efficient online CT will especially open a vast number of possibilities for optimising the production.