Computed tomography (CT) has successfully been applied in medical environments for decades. In recent years CT has also made its entry to the industrial environments, including the slaughterhouses. In this paper we investigate classication methods for an online CT system, in order to assist in the segmentation of the outer fat layer in the mid- section of CT-scanned pig carcasses. Prior information about the carcass composition can potentially be applied for a fully automated solution, in order to optimize the slaughter line. The methods comprise Markov Random Field and contextual Bayesian classication, and are adapted to use neighbourhood information in 2D and 3D. Articial Poisson noise is added to the provided dataset to determine how well each of the methods handles noise. Good noise handling will allow lower dose scannings. The investigated methods did not perform better than the reference model in terms of classication, but the MRF segmentation showed promising results in a case with extreme simulated noise.