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High Resolution CT (HRCT) techniques developed in the last decade have become invaluable tools for the detection of subtle diffuse lung disease patterns and for their characterisation into multiple possible diseases. HRCT imaging protocols produce 3D volume data and enable accurate visualisation of imaged anatomy and much better visualisation of the disease patterns than conventional X-rays. However, the amount of information produced by today’s HRCT scanners is beyond the ability of a radiologist to process in normal clinical practice. Single detector scanners generate up to 40 images per study and multi-slice detectors generate 300-600 high-resolution axial images. Furthermore, the number of images is rapidly growing. It is difficult and time consuming to analyse images accurately and efficiently by hand. Systems for computerised image analysis are needed to help with the large number of images and to draw radiologist’s attention to fewer, diagnostically useful images. The goal of computerised medical image analysis and interpretation is to detect abnormal appearance of the imaged anatomy and to assist radiologists in identifying and integrating all the useful information available in an image (Brown & McNitt-Gray, 2000). There is a growing number of computer-aided diagnosis (CAD) systems aimed at automating the analysis of lung CT images and supporting diagnosis (Uppaluri, et al., 1999; Uchiyama et al., 2003; Sluimer, 2005; Zrimec et al., 2007; Tolouee et al., 2008). Uppaluri et al. (1999) presented a CAD system for detecting six lung tissue patterns using textural features. A multiple feature method was used to determine the optimal subset among 22 textural features calculated for each 31x31 pixel square region of interest in an image. A Bayesian classifier was trained to use the optimal subset of features to recognize six different tissue patterns. They reported that the automated system performed as well as experienced human observers who were told the diagnosis in advance. Uchiyama et al. (2003) also divided the lung into square regions and employed neural networks to perform classification of HRCT images into six textural classes. The neural network, trained with examples of different tissue patterns, was able to automatically detect images containing abnormalities and to provide good classification. In the work reported by Sluimer (2005), a multi-scale filter bank was used to represent the local image texture and structure. They used various classifiers to |