The research presented in this article is aimed at developing an automated imaging system for classification of tissues in medical images. The article focuses on using texture analysis for the classification of tissues from CT scans. The approach consists of two steps: automatic extraction of the most discriminative texture features of regions of interest in the CT medical images and creation of a classifier that will automatically identify the various tissues. A comparative study of wavelets-based texture descriptors from three families of wavelets (Haar, Daubechies, Coiflets), coupled with the implementation of a decision tree classifier based on the Classification and Regression Tree (C&RT) approach is carried on. Preliminary results for a 3D data set from normal chest and abdomen CT scans are presented.