3D DCT supervised segmentation applied on liver volumes

Liver segmentation from computer tomography scans is a topic of research interest, as the acquisition and inter-patient variability make the automatic segmentation difficult. The current trend is to improve the accuracy and to reduce the computational complexity of the segmentation, as this is essential for the diagnostic and for 3D rendering. We propose a new computationally efficient approach for 3D liver segmentation, based on the 3D Discrete Cosine Transform applied on volume blocks for feature extraction, followed by a support vector machine classification of volume blocks. The segmentation is refined in a post-processing step through a 3D median filtering, 3D morphological operations, and 3D connected components analysis. This new method has been applied on real liver volumes and provided promising results, on the level of the state of the art, with a significant reduction in the data to be processed and in the operations involved as compared to other approaches.