Texture analysis is one of the key techniques of image understanding and processing with widespread applications from low level image segmentation to high level object recognition. Gaussian Markov random field (GMRF) is a particular model based texture feature extraction scheme which uses model parameters as texture features. In this thesis a novel robust texture descriptor based on GMRF is proposed specially for texture segmentation and classification. For these tasks, descriptive features are more favourable relative to the generative features. Therefore, in order to achieve more descriptive features, with the GMRFs, a localized parameter estimation technique is introduced here. The issues arising in the localized parameter estimation process, due to the associated small sample size, are addressed by applying Tikhonov regularization and an estimation window size selection criterion. The localized parameter estimation process proposed here can overcome the problem of parameter smoothing that occurs in traditional GMRF parameter estimation. Such a parameter smoothing disregards some important structural and statistical information for texture discrimination. The normalized distributions of local parameter estimates are proposed as the new texture features and are named as Local Parameter Histogram (LPH) descriptors. Two new rotation invariant texture descriptors based on LPH features are also introduced, namely Rotation Invariant LPH (RI-LPH) and Isotropic LPH (I-LPH)descriptors. The segmentation and classification results on large texture datasets demonstrate that these descriptors significantly improve the performance of traditional GMRF features and also demonstrate better performance in comparison with the state-of-the-art texture descriptors. Satisfactory natural image segmentation is also carried out based on the novel features. Furthermore, proposed features are employed in a real world medical application involving tissue recognition for emphysema, a critical lung disease causing lung tissue destruction. Features extracted from High Resolution Computed Tomography (HRCT) data are used in effective tissue recognition and pathology distribution diagnosis. Moreover, preliminary work on a Bayesian framework for integrating prior knowledge into the parameter estimation process is undertaken to introduce further improved texture features.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:632625 |
| Date | January 2014 |
| Creators | Dharmagunawardhana, Chathurika |
| Contributors | Mahmoodi, Sasan |
| Publisher | University of Southampton |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://eprints.soton.ac.uk/372489/ |
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