Biomedical images contain a great deal of information that is useful and a great deal that is not. Computational analysis and interpretation of biomedical images involves extraction of some or all of the useful information. The useless information can take the form of unwanted clutter or noise that can obscure the useful information or inhibit the interpretation. Various mathematical and computational processes may be applied to reduce the effects of noise and distracting content. The most successful approaches involve the use of mathematical or computational models that express the properties of the required information. Interpretation of images involves finding objects or structures in the image that match the properties of the model. This dissertation describes the development and application of different models required for the interpretation of a variety of different image types arising from clinical medicine or biomedical research. These include:* neural network models, * Point Distribution Models, and the associated Active Shape Models, which have become part of the research toolkit of many academic and commercial organisations, * models of the appearance of nerve fibres in noisy confocal microscope images,* models of pose changes in carpal bones during wrist motion, A number of different application problem are described, in which variants of these methods have been developed and used: * cytogenetics, * proteomics, * assessing bone quality, * segmentation of magnetic resonance images, * measuring nerve fibres * inferring 3D motion from 2D cinefluoroscopy sequences. The methods and applications represented here encompass the progression of biomedical image analysis from early developments, where computational power became adequate to the challenges posed by biomedical image data, to recent, highly computationally-intensive methods.

Triangle-mesh modeling, as one of the approaches for representing images based on nonuniform sampling, has become quite popular and beneficial in many applications. In this thesis, image representation using triangle-mesh models and its application in image scaling are studied. Consequently, two new methods, namely, the SEMMG and MIS methods are proposed, where each solves a different problem. In particular, the SEMMG method is proposed to address the problem of image representation by producing effective mesh models that are used for representing grayscale images, by minimizing squared error. The MIS method is proposed to address the image-scaling problem for grayscale images that are approximately piecewise-smooth, using triangle-mesh models. The SEMMG method, which is proposed for addressing the mesh-generation problem, is developed based on an earlier work, which uses a greedy-point-insertion (GPI) approach to generate a mesh model with explicit representation of discontinuities (ERD). After in-depth analyses of two existing methods for generating the ERD models, several weaknesses are identified and specifically addressed to improve the quality of the generated models, leading to the proposal of the SEMMG method. The performance of the SEMMG method is then evaluated by comparing the quality of the meshes it produces with those obtained by eight other competing methods, namely, the error-diffusion (ED) method of Yang, the modified Garland-Heckbert (MGH) method, the ERDED and ERDGPI methods of Tu and Adams, the Garcia-Vintimilla-Sappa (GVS) method, the hybrid wavelet triangulation (HWT) method of Phichet, the binary space partition (BSP) method of Sarkis, and the adaptive triangular meshes (ATM) method of Liu. For this evaluation, the error between the original and reconstructed images, obtained from each method under comparison, is measured in terms of the PSNR. Moreover, in the case of the competing methods whose implementations are available, the subjective quality is compared in addition to the PSNR. Evaluation results show that the reconstructed images obtained from the SEMMG method are better than those obtained by the competing methods in terms of both PSNR and subjective quality. More specifically, in the case of the methods with implementations, the results collected from 350 test cases show that the SEMMG method outperforms the ED, MGH, ERDED, and ERDGPI schemes in approximately 100%, 89%, 99%, and 85% of cases, respectively. Moreover, in the case of the methods without implementations, we show that the PSNR of the reconstructed images produced by the SEMMG method are on average 3.85, 0.75, 2, and 1.10 dB higher than those obtained by the GVS, HWT, BSP, and ATM methods, respectively. Furthermore, for a given PSNR, the SEMMG method is shown to produce much smaller meshes compared to those obtained by the GVS and BSP methods, with approximately 65% to 80% fewer vertices and 10% to 60% fewer triangles, respectively. Therefore, the SEMMG method is shown to be capable of producing triangular meshes of higher quality and smaller sizes (i.e., number of vertices or triangles) which can be effectively used for image representation. Besides the superior image approximations achieved with the SEMMG method, this work also makes contributions by addressing the problem of image scaling. For this purpose, the application of triangle-mesh mesh models in image scaling is studied. Some of the mesh-based image-scaling approaches proposed to date employ mesh models that are associated with an approximating function that is continuous everywhere, which inevitably yields edge blurring in the process of image scaling. Moreover, other mesh-based image-scaling approaches that employ approximating functions with discontinuities are often based on mesh simplification where the method starts with an extremely large initial mesh, leading to a very slow mesh generation with high memory cost. In this thesis, however, we propose a new mesh-based image-scaling (MIS) method which firstly employs an approximating function with selected discontinuities to better maintain the sharpness at the edges. Secondly, unlike most of the other discontinuity-preserving mesh-based methods, the proposed MIS method is not based on mesh simplification. Instead, our MIS method employs a mesh-refinement scheme, where it starts from a very simple mesh and iteratively refines the mesh to reach a desirable size. For developing the MIS method, the performance of our SEMMG method, which is proposed for image representation, is examined in the application of image scaling. Although the SEMMG method is not designed for solving the problem of image scaling, examining its performance in this application helps to better understand potential shortcomings of using a mesh generator in image scaling. Through this examination, several shortcomings are found and different techniques are devised to address them. By applying these techniques, a new effective mesh-generation method called MISMG is developed that can be used for image scaling. The MISMG method is then combined with a scaling transformation and a subdivision-based model-rasterization algorithm, yielding the proposed MIS method for scaling grayscale images that are approximately piecewise-smooth. The performance of our MIS method is then evaluated by comparing the quality of the scaled images it produces with those obtained from five well-known raster-based methods, namely, bilinear interpolation, bicubic interpolation of Keys, the directional cubic convolution interpolation (DCCI) method of Zhou et al., the new edge-directed image interpolation (NEDI) method of Li and Orchard, and the recent method of super-resolution using convolutional neural networks (SRCNN) by Dong et al.. Since our main goal is to produce scaled images of higher subjective quality with the least amount of edge blurring, the quality of the scaled images are first compared through a subjective evaluation followed by some objective evaluations. The results of the subjective evaluation show that the proposed MIS method was ranked best overall in almost 67\% of the cases, with the best average rank of 2 out of 6, among 380 collected rankings with 20 images and 19 participants. Moreover, visual inspections on the scaled images obtained with different methods show that the proposed MIS method produces scaled images of better quality with more accurate and sharper edges. Furthermore, in the case of the mesh-based image-scaling methods, where no implementation is available, the MIS method is conceptually compared, using theoretical analysis, to two mesh-based methods, namely, the subdivision-based image-representation (SBIR) method of Liao et al. and the curvilinear feature driven image-representation (CFDIR) method of Zhou et al.. / Graduate

Since the 1970s when the first destination image studies were performed, this topic has become one of the most predominant in the tourism marketing literature. Destination image within the tourism industry is essential, as most tourism products are services rather than physical goods, and can often only compete by means of the image they portray. The image of a specific destination is a major element in the final decision when selecting the destination. Both positive and negative images occur, together having a great impact on the travel and tourism industry. Destinations therefore have to create images of their location and what they have to offer to help differentiate them from their competition. Therefore, potential tourists rely on their mental images when deciding to visit one destination over another. Different influences emerge within tourist decisions, which affect their ultimate experience. It is therefore clear that, to understand tourists‟ needs and wants, relationship building is important and this could assist with the marketing of products or services. Marketing plays a central part in tourism, since consumers need to travel to a certain destination to see, feel or test the product that is to be purchased and evaluated. Image is formed based on three main components. These are: cognitive (what one knows about a destination), affective (how one feels about what one knows) and conative components (how one acts on this information). To date, various image models have been developed. However, none of these have been applied to, tested in, or developed for South Africa. It is therefore important to know how tourists formulate a destinations‟ image as well as what influences their image regarding a destination. Therefore, to achieve this and the goal of this study, which is to develop a framework for the optimisation of the image of South Africa as a tourism destination, a comprehensive review of marketing and destination image literature was performed, subsequent to which the research was conducted. After having conducted the literature review and gathered expert advice and opinions, various literature-based attributes were identified. A total of sixty-three attributes were acknowledged whereafter these were sifted and grouped into Cognitive, Affective and Conative attributes. After taking expert advice into consideration, these attributes were once again sifted and it was determined whether they were applicable for this research. A total of fifty-seven attributes remained important and formed part of the questionnaire. Forty-two attributes were Cognitive, twelve Affective and three Conative. The research was conducted at the international departure area of a major international airport in South Africa. The respondents consisted of international tourists that were returning to their home countries after visiting South Africa. A total of 500 questionnaires were distributed of which 474 questionnaires were obtained. Of these, 451 questionnaires were usable for this study, as 23 questionnaires were incomplete and not usable. The number of questionnaires was therefore representative of the target population and further analysis. After the questionnaires for this study were gathered, the primary data was captured and analysed. Different types of data analyses were used in this study: Firstly, descriptive analysis to determine findings concerning the demographic profile of respondents and the respondent‟s travel behaviour whilst visiting South Africa. Secondly, factor analyses to factorise the image attributes into image factors; and to factorise external aspects into factors and determine how these affect image formation. Thirdly, ANOVAs (One-way analysis of variance) were conducted where more than two categories formed part of the question, t-tests were conducted to compare the image factors with questions consisting of only two categories and Spearman rank correlations were conducted to describe the strength and direction of the linear relationship between selected variables. Finally, Structural Equation Modelling was used to empirically test the framework and evaluate how well the data supports the hypothesised model. The first factor analysis resulted in 13 reliable and valid factors, which consisted of the cognitive, affective and conative image attributes. These factors, together with the factors of the second factor analysis (Media, Political and Iconic aspects) were used as constructs in the Structural Equation Modelling analysis. After having combined the results of all the different analyses, a framework was developed that identifies the aspects influencing South Africa‟s image. Some of the main findings were that media, political happenings and iconic aspects directly influenced cognitive, affective and conative images. Novel to this study was the significant influence of icons. Interestingly, demographic information only affects cognitive image and neither affective nor conative image. Travel behaviour contributes to the formation of cognitive, affective and conative image.However, surprisingly, the lack of influence from travel agents and travel guides was also depicted in the results. This framework emphasises the importance of pre-, onsite and post-experiences as well as communication in image formation. This study contributes academically, methodologically and practically. Academic contributions include empirically testing the framework, which significantly contributes to literature; and the innovative inclusion and assessment of icons adds a new dimension to image formation in literature. From a methodological point of view, it is clear that the analyses of all influencing aspects are challenging and not standardised. The types of analyses applied in this study enhanced the in-depth analyses of the data that was then included into one framework. The data was empirically tested and found to be reliable. The empirical testing of all aspects in a South African context was different and innovative, which finally created a detailed picture of South Africa‟s image as a tourism destination. Finally, the practical contribution of this study is that the framework developed for this study can be used by tourism organisations of various types in planning and implementing marketing strategies. The framework can direct their advertising and staff training; and improve the general tourism product of South Africa. The framework can also be applied to other tourism destinations. Clear recommendations were made regarding the focus of marketing strategies and building the image of South Africa. It was recommended that the framework developed in this study be implemented by national tourism organisations such as SA Tourism, as well as provincial organisations such as Tourism Boards. Product owners can benefit from the framework by considering some of the influential aspects in their product development and marketing strategies. Lastly, all marketing strategies and plans for South Africa should be focused on improving the cognitive, affective and conative image of South Africa. / PhD (Tourism Management), North-West University, Potchefstroom Campus, 2015

Since the 1970s when the first destination image studies were performed, this topic has become one of the most predominant in the tourism marketing literature. Destination image within the tourism industry is essential, as most tourism products are services rather than physical goods, and can often only compete by means of the image they portray. The image of a specific destination is a major element in the final decision when selecting the destination. Both positive and negative images occur, together having a great impact on the travel and tourism industry. Destinations therefore have to create images of their location and what they have to offer to help differentiate them from their competition. Therefore, potential tourists rely on their mental images when deciding to visit one destination over another. Different influences emerge within tourist decisions, which affect their ultimate experience. It is therefore clear that, to understand tourists‟ needs and wants, relationship building is important and this could assist with the marketing of products or services. Marketing plays a central part in tourism, since consumers need to travel to a certain destination to see, feel or test the product that is to be purchased and evaluated. Image is formed based on three main components. These are: cognitive (what one knows about a destination), affective (how one feels about what one knows) and conative components (how one acts on this information). To date, various image models have been developed. However, none of these have been applied to, tested in, or developed for South Africa. It is therefore important to know how tourists formulate a destinations‟ image as well as what influences their image regarding a destination. Therefore, to achieve this and the goal of this study, which is to develop a framework for the optimisation of the image of South Africa as a tourism destination, a comprehensive review of marketing and destination image literature was performed, subsequent to which the research was conducted. After having conducted the literature review and gathered expert advice and opinions, various literature-based attributes were identified. A total of sixty-three attributes were acknowledged whereafter these were sifted and grouped into Cognitive, Affective and Conative attributes. After taking expert advice into consideration, these attributes were once again sifted and it was determined whether they were applicable for this research. A total of fifty-seven attributes remained important and formed part of the questionnaire. Forty-two attributes were Cognitive, twelve Affective and three Conative. The research was conducted at the international departure area of a major international airport in South Africa. The respondents consisted of international tourists that were returning to their home countries after visiting South Africa. A total of 500 questionnaires were distributed of which 474 questionnaires were obtained. Of these, 451 questionnaires were usable for this study, as 23 questionnaires were incomplete and not usable. The number of questionnaires was therefore representative of the target population and further analysis. After the questionnaires for this study were gathered, the primary data was captured and analysed. Different types of data analyses were used in this study: Firstly, descriptive analysis to determine findings concerning the demographic profile of respondents and the respondent‟s travel behaviour whilst visiting South Africa. Secondly, factor analyses to factorise the image attributes into image factors; and to factorise external aspects into factors and determine how these affect image formation. Thirdly, ANOVAs (One-way analysis of variance) were conducted where more than two categories formed part of the question, t-tests were conducted to compare the image factors with questions consisting of only two categories and Spearman rank correlations were conducted to describe the strength and direction of the linear relationship between selected variables. Finally, Structural Equation Modelling was used to empirically test the framework and evaluate how well the data supports the hypothesised model. The first factor analysis resulted in 13 reliable and valid factors, which consisted of the cognitive, affective and conative image attributes. These factors, together with the factors of the second factor analysis (Media, Political and Iconic aspects) were used as constructs in the Structural Equation Modelling analysis. After having combined the results of all the different analyses, a framework was developed that identifies the aspects influencing South Africa‟s image. Some of the main findings were that media, political happenings and iconic aspects directly influenced cognitive, affective and conative images. Novel to this study was the significant influence of icons. Interestingly, demographic information only affects cognitive image and neither affective nor conative image. Travel behaviour contributes to the formation of cognitive, affective and conative image.However, surprisingly, the lack of influence from travel agents and travel guides was also depicted in the results. This framework emphasises the importance of pre-, onsite and post-experiences as well as communication in image formation. This study contributes academically, methodologically and practically. Academic contributions include empirically testing the framework, which significantly contributes to literature; and the innovative inclusion and assessment of icons adds a new dimension to image formation in literature. From a methodological point of view, it is clear that the analyses of all influencing aspects are challenging and not standardised. The types of analyses applied in this study enhanced the in-depth analyses of the data that was then included into one framework. The data was empirically tested and found to be reliable. The empirical testing of all aspects in a South African context was different and innovative, which finally created a detailed picture of South Africa‟s image as a tourism destination. Finally, the practical contribution of this study is that the framework developed for this study can be used by tourism organisations of various types in planning and implementing marketing strategies. The framework can direct their advertising and staff training; and improve the general tourism product of South Africa. The framework can also be applied to other tourism destinations. Clear recommendations were made regarding the focus of marketing strategies and building the image of South Africa. It was recommended that the framework developed in this study be implemented by national tourism organisations such as SA Tourism, as well as provincial organisations such as Tourism Boards. Product owners can benefit from the framework by considering some of the influential aspects in their product development and marketing strategies. Lastly, all marketing strategies and plans for South Africa should be focused on improving the cognitive, affective and conative image of South Africa. / PhD (Tourism Management), North-West University, Potchefstroom Campus, 2015