This work presents an analytic three dimensional image reconstruction algorithm that was developed for a proposed volume-imaging PET scanner. The development of the algorithm was motivated by the scanner's ability to collect an order of magnitude more data than current PET systems and the lack of an efficient algorithm that could use the extra data. The algorithm is based on an extension of the Recovery Operator of Orlov[68] and operates by convolution in object space. This method of operation sets it apart from other analytic direct image reconstruction algorithms that rely on Fourier transforms. The algorithm is tested with ideal data and parameters that are appropriate to the new PET scanner. The results of the test show that the algorithm behaves as expected except for a 17% overshoot in the reconstructed value in one area. An explanation of this artifact is suggested, although not verified. Finally, the efficacy of the algorithm is demonstrated by proving that it is functionally equivalent to Fourier transform methods. / Science, Faculty of / Physics and Astronomy, Department of / Graduate

Currently, standard enhancement of three-channel colour digital imagery is not performed in a general fashion; it is dependent on the particular colour device used to display the imagery. By introducing a colour transformation, the enhancement can be standardized and therefore be defined in terms of other devices. In addition, the colour transformation can be defined such that the perceptual attributes of colour associated with an image are more easily manipulated. The purpose of this work is to examine a variety of such colour transformations and implement a subset of these on a colour CRT. / Science, Faculty of / Computer Science, Department of / Graduate

Many applications of image analysis and image processing involve the alteration of the geometry of real images. This thesis presents the theory and implementation of three types of geometrically altered imagery: synthetic orthographic stereo pairs, synthetic perspective stereo pairs, and synthetic airborne scanner imagery. The real image is altered as a function of a corresponding surface model. Included is a determination of surface points which are hidden under the assumed imaging geometry and, in the case of radar systems, a determination of surface points which contribute to pixel layover. In this research, a digital terrain model determines the underlying surface geometry of the real image. / Science, Faculty of / Computer Science, Department of / Graduate

M. Ing. (Mechanical Engineering) / This research aims at identifying techniques which can be used to remove rain from a digitized rain distorted image. The research commenced with computer implemented rain removal techniques but also lead to camera obscuration techniques. Camera rain obscuration technique: Before a rainy scene is digitized, rain can be removed from the projected image by the correct selection of camera aperture and shutter speed settings. These settings will determine the way the real image is transformed through the lens and onto the recording plane. Computer image restoration technique: Rain removal after digitization is performed in both the frequency and the spatial domain by means of two dimensional image processing and filtering techniques. Time consuming frequency domain techniques were replaced by equivalent convolutional techniques. Experiments on simulated and real scenes corrupted with rain indicated that it is possible to improve the image appearance with only a marginal decrease in signal to noise ratio.

Bibliography: pages 174-182. / This dissertation investigates the machine vision grading of flue-cured Virginia tobacco by means of digital processing of tobacco leaf images. With reference to international grading standards and to modem image processing techniques, two classifiers are designed. The colour classifier uses seven features extracted from each leaf image to grade the leaf into one of five official colour classes. It does this with an expected correct classification rate of 93.5%. The plant position classifier identifies the position on the stalk from which a leaf was reaped, using ten size and shape features to classify the leaf into one of six plant position categories. It has a correct classification rate of 70%. Average colours for each colour class and archetypal shapes for each plant position category are derived from the digital leaf data. These should be of value to tobacco graders as objective representations of typical leaves within each class.

Tensors are of great interest to many applications in engineering and in medical imaging, but a proper analysis and visualization remains challenging. It already has been shown that, by employing the metaphor of a fabric structure, tensor data can be visualized precisely on surfaces where the two eigendirections in the plane are illustrated as thread-like structures. This leads to a continuous visualization of most salient features of the tensor data set. We introduce a novel approach to compute such a visualization from tensor field data that is motivated by image-space line integral convolution (LIC). Although our approach can be applied to arbitrary, non-selfintersecting surfaces, the main focus lies on special surfaces following important features, such as surfaces aligned to the neural pathways in the human brain. By adding a postprocessing step, we are able to enhance the visual quality of the of the results, which improves perception of the major patterns.

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Diffuse Lung Diseases (DLD), impute to 15% of respiratory practice and are accountable for a large class of disorders, primarily affecting lung parenchyma. As a part of the diagnostic workup by the physician, a chest CT image is often required in addition to a thorough medical history and physical examination. The reliable identification of the features among interstitial lung diseases and the patterns they may take is challenging, particularly given the volume of data on a CT scan that must be processed by the radiologist. It has been shown that even among expert chest radiologists there is significant inter-observer and intra-observer variability. To make an objective quantitative and qualitative assessment of lung disease patterns, an accurate and reliable computer aided diagnostic system is likely to be extremely useful to assist with dealing with data volume for an expert radiologist. There will also be the opportunity to improve sensitivity and specificity in a non-expert radiologist group. Literature suggests that computer based pattern classifiers can discern image abnormalities due to lung diseases such as consolidation, cyst, emphysema, fibrosis, ground glass opacity, honey combing, nodularity, reticulation, scar and tree-in-bud. Researchers have focused on developing algorithms to quantify and analyse the surface changes of the lung, since DLD patterns often manifest as texture differences within the lung parenchyma. Research reported in this thesis has incorporated texture quantification, fractal analysis and scale invariant feature transform methods as complementary feature extraction techniques to improve the classification accuracy, especially in the presence of large number of classes associated with interstitial diseases. Classification of ten lung pathologies and healthy lung regions are validated based on different combination of diseases using leave-one-out and 5-fold cross validation techniques and an Artificial Neural Network (ANN). Classification accuracy based on features selected using scale invariant feature transform method alone generates 99% accuracy for up to four classes and more than 71% for up to eleven classes using an ANN. Classification accuracy is 85% for eleven classes using a combination of scale invariant feature transform, texture and fractal based features. Classification accuracies improve for higher number of classes (> 5) when the combination of above mentioned features are incorporated. Detailed classification accuracies for several DLD features compared to a healthy lung, and combinations of DLD features, such as fibrosis, reticulation, honey combing in comparison with healthy lung are evaluated throughout this thesis. / Thesis / Doctor of Philosophy (PhD)

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