A New Segmentation Framework for in vivo Internal Organs Removal of CT Scans of Pigs

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 meat, fat and bone from a set of images from Computed Tomography scans. The main challenge is the detection of the internal organs, such as the lungs, the liver, and the kidneys, which are not considered in the manual dissection. The proposed approach has been tested on real data sets, and compared with manual dissection. The obtained results prove the high correlation between the virtual and manual dissection.