Handbook of pattern recognition & computer vision
A Statistical Approach to Texture Classification from Single Images
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Wavelet-Based Texture Classification of Tissues in Computed Tomography
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Texture-Based Image Retrieval for Computerized Tomography Databases
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Texture information in run-length matrices
IEEE Transactions on Image Processing
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In this paper we discuss the problem of discriminating tissues with similar average Hounsfield values in Computed Tomography (CT) images through the use of supervised classification of feature vectors computed on small texture patches. We point out the differences between this problem and classical texture classification workbenches and analyze the role of data pre-processing (depth subsampling, equalization) in determining how well classical texture features based on Gray Level Run Length Matrices (GLRLM) and Gray Level Co-Occurrence Matrices (GLCM), discriminate tissues. Depth reduction and contrast stretching are shown to be key factors determining the information captured by features and can be interpreted as a "material segmentation". Theory and experimental results show that different pre-processing does change error rates of supervised classifiers trained with GLRLM and GLCM based descriptors and that, using optimal depth subsampling methods it is possible to obtain good texture classification results compatible with a physical interpretation of texture elements. On the basis of this interpretation, a new simple descriptor using ad hoc image thresholding and shape analysis is also introduced and compared with the previously discussed methods as well as with wavelet-based multi-resolution filtering.