Primary Live Cancer (PLC) is the sixth most common cancer worldwide and its occurrence predominates in patients with chronic liver diseases and other risk factors like hepatitis B and C. Treatment of PLC and malignant liver tumors depend both in tumor characteristics and the functional status of the organ, thus must be individualized for each patient. Liver segmentation and classification according to Couinaud's classification is essential for computer-aided diagnosis and treatment planning, however, manual segmentation of the liver volume slice by slice can be a time-consuming and challenging task and it is highly dependent on the experience of the user. We propose an alternative automatic segmentation method that allows accuracy and time consumption amelioration. The procedure pursues a multi-atlas based classification for Couinaud segmentation. Our algorithm was implemented on 20 subjects from the IRCAD 3D data base in order to segment and classify the liver volume in its Couinaud segments, obtaining an average DICE coefficient of 0.94.Clinical Relevance- The final purpose of this work is to provide an automatic multi-atlas liver segmentation and Couinaud classification by means of CT image analysis.