Medical image processing fuses the image processing technologies in the medical disciplines. Particularly, computed tomography images provide a 3D vision of any part of the human body. These 3D images are generated by CT-Scanner devices. In this paper, we propose an advanced method of CT-Scanner identification from its 3D images. Basically, we analyze the sensor noise in order to identify the source CT-Scanner. For each CT-Scanner, we build three dimension identifiers regarding the three directional axes `X', `Y' and `Z'. The dimension identifier consists of a reference pattern noise and a correlation map. To identify the source CT-Scanner from a tested slice, we compute the correlation between each dimension identifier of each device and this tested slice. The highest correlation value represents an indicator to the source CT-Scanner and the acquisition directional axis. To isolate the pure noise, we use a wavelet based denoising algorithm. Experiments are applied on three different CT-Scanners. 10 3D images are selected from each CT-Scanner, each 3D image is composed of 512 slices. As a result, we are able to identify the acquisition CT-Scanner and the acquisition dimensional axis.