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Here we aim to evaluate the pulmonary parenchyma from CT scans of the thorax using textural analysis. For each of 34 patients, 3 axial slices were chosen. We split each of the 102 images into grids with block sizes of 4, 8 and 16 pixels and calculated 18 textural features for each block. Using these features and a training set assembled by a radiologist, we train a support vector machine (SVM) to recognise some typical patterns found on the scans and test the accuracy on the training set using cross-validation. Then, larger areas deemed broadly representative of each of the patterns under consideration were labelled on the 102 images and the classification accuracy for each pattern and each block size is presented. Using the classified images, we segment the lung regions using a variation of the normal method. Finally, we fuse the results from the 3 block sizes to form a single image using Naive Bayes and show this matches or improves on the accuracy using each of the individual block sizes alone.