Measuring human subcutaneous fat is useful for assessing health risks due to obesity and for monitoring athletes’ health status, body shapes and weight for various sports competitions such as gymnastics and wrestling. Our aim is to investigate the use of ultrasound imaging in automatically measuring human subcutaneous fat thickness. We proposed to use the spectrum properties extracted from the raw radio frequency (RF) signals of ultrasound for the purpose of fat boundary detection. Our fat detection framework consists of four main steps. The first step is capturing RF data from 11 beam steering angles and at four focal positions. Secondly, two spectrum properties (spectrum variance and integrated backscatter coefficient) are calculated from the local spectrum of RF data using the short time Fourier transform and moment analysis. The values of the spectrum properties are encoded as gray-scale parametric images. Thirdly, spatial compounding is used to reduce speckle noise in the parametric images and improve the visualization of the subcutaneous fat layer. Finally, we apply Rosin’s thresholding and Random Sample Consensus boundary detection on the parametric images to extract the fat boundary. The detection framework was tested on 36 samples obtained at the suprailiac, thigh and triceps of nine human participants in vivo. When compared to manual boundary detection on ultrasound images, the best result was obtained from segmenting the spatial compounded spectrum variance values averaged over multiple focuses. A reasonable result could also be obtained by using a single focus. Further, our automatic detection results were compared with the results using skinfold caliper measurements. We found that the correlation is high between our automatic detection and skinfold caliper measurement, and is similar to the previous studies which are not automatic. Our work has shown that the spatial compounded spectrum properties of RF data can be used to segment the subcutaneous fat layer. Based on our results, it is feasible to detect fat at the suprailiac, thigh and triceps sites using the spectrum variance. The values of spectrum variance change more rapidly in the fat tissue than the non-fat tissue.

Medical image analysis is a ubiquitous and essential part of modem health care. A crucial first step to this is segmentation, which is often complicated by many factors including subject diversity, pathology, noise corruption, and poor image resolution. Traditionally, manual tracing by experts was done. While considered accurate, this process is time consuming and tedious, especially when performed slice-by-slice on three-dimensional (3D) images over large datasets or on two-dimensional (2D) but topologically complicated images such as a retinography. On the other hand, fully-automated methods are typically faster, but work best with data-dependent, carefully tuned parameters and still require user validation and refinement. This thesis contributes to the field of medical image segmentation by proposing a highly-automated, interactive approach that effectively merges user knowledge and efficient computing. To this end, our work focuses on graph-based methods and offer globally optimal solutions. First, we present a novel method for 3D segmentation based on a 3D Livewire approach. This approach is an extension of the 2D Livewire framework, and this method is capable of handling objects with large protrusions, concavities, branching, and complex arbitrary topologies. Second, we propose a method for efficiently segmenting 2D vascular networks, called ‘Live-Vessel’. Live-Vessel simultaneously extracts vessel centrelines and boundary points, and globally optimizes over both spatial variables and vessel radius. Both of our proposed methods are validated on synthetic data, real medical data, and are shown to be highly reproducible, accurate, and efficient. Also, they were shown to be resilient to high amounts of noise and insensitive to internal parameterization.

With the ever-increasing number of people accessing the Internet and the recent explosion of e-commerce world wide, there are considerable implications for the tourism industry. Tourism suppliers are investing in the Internet via web pages, advertising and e-commerce, but what role does the Internet actually play in tourism? Before more money is placed into this new 'e-economy', it is important to study the effectiveness of the Internet as a marketing tool in tourism. In order to better address the concerns described above, this research accomplishes several tasks. First, the significance of researching Internet use within the tourism context is established. Specifically, theories and concepts from postmodernism, post-industrialism and post-structuralism are drawn upon as they frame this study. Second, this research explores motivation and decision making within tourism and how the Internet is used during stages of travel preparation, planning and activities. Third, this research explores tourist preferences for novelty and familiarity in three dimensions; travel services, social contact and destination choices, and examines how these are associated with Internet use. The general structure of tourism markets in relation to Internet use as well as novelty and familiarity preferences are also discussed. Three case studies are undertaken to examine these matters: winter tourists, summer tourists and cruise tourists. Novelty-seekers were found to be the most frequent group of Internet users, and also were the most likely to consult a wider variety of information sources when making travel-related decisions. Results also indicate that Internet use for travel varies according to seasonality and destination choices rather than primary activity.

Iris recognition systems capture an image from an individual's eye. The iris in the image is then segmented and normalized for feature extraction process. The performance of iris recognition systems highly depends on segmentation and normalization. For instance, even an effective feature extraction method would not be able to obtain useful information from an iris image that is not segmented or normalized properly. This thesis is to enhance the performance of segmentation and normalization processes in iris recognition systems to increase the overall accuracy. <br /><br /> The previous iris segmentation approaches assume that the boundary of pupil is a circle. However, according to our observation, circle cannot model this boundary accurately. To improve the quality of segmentation, a novel active contour is proposed to detect the irregular boundary of pupil. The method can successfully detect all the pupil boundaries in the CASIA database and increase the recognition accuracy. <br /><br /> Most previous normalization approaches employ polar coordinate system to transform iris. Transforming iris into polar coordinates requires a reference point as the polar origin. Since pupil and limbus are generally non-concentric, there are two natural choices, pupil center and limbus center. However, their performance differences have not been investigated so far. We also propose a reference point, which is the virtual center of a pupil with radius equal to zero. We refer this point as the linearly-guessed center. The experiments demonstrate that the linearly-guessed center provides much better recognition accuracy. <br /><br /> In addition to evaluating the pupil and limbus centers and proposing a new reference point for normalization, we reformulate the normalization problem as a minimization problem. The advantage of this formulation is that it is not restricted by the circular assumption used in the reference point approaches. The experimental results demonstrate that the proposed method performs better than the reference point approaches. <br /><br /> In addition, previous normalization approaches are based on transforming iris texture into a fixed-size rectangular block. In fact, the shape and size of normalized iris have not been investigated in details. In this thesis, we study the size parameter of traditional approaches and propose a dynamic normalization scheme, which transforms an iris based on radii of pupil and limbus. The experimental results demonstrate that the dynamic normalization scheme performs better than the previous approaches.

Medical imaging plays a vital role in the medical field because it is widely used in diseases diagnosis and treatment of patients. There are different modalities of medical imaging such as ultrasounds, x-rays, Computed Tomography (CT), Magnetic Resonance (MR), and Positron Emission Tomography (PET). Most of these modalities usually suffer from noise and other sampling artifacts. The diagnosis process in these modalities depends mainly on the interpretation of the radiologists. Consequently, the diagnosis is subjective as it is based on the radiologist experience. Medical image segmentation is an important process in the field of image processing. It has a significant role in many applications such as diagnosis, therapy planning, and advanced surgeries. There are many segmentation techniques to be applied on medical images. However, most of these techniques are still depending on the experts, especially for initializing the segmentation process. The artifacts of images can affect the segmentation output. In this thesis, we propose a new approach for automatic prostate segmentation of Trans-Rectal UltraSound (TRUS) images by dealing with the speckle not as noise but as informative signals. The new approach is an automation of the conventional region growing technique. The proposed approach overcomes the requirement of manually selecting a seed point for initializing the segmentation process. In addition, the proposed approach depends on unique features such as the intensity and the spatial Euclidean distance to overcome the effect of the speckle noise of the images. The experimental results of the proposed approach show that it is fast and accurate. Moreover, it performs well on the ultrasound images, which has the common problem of the speckle noise.

With the ever-increasing number of people accessing the Internet and the recent explosion of e-commerce world wide, there are considerable implications for the tourism industry. Tourism suppliers are investing in the Internet via web pages, advertising and e-commerce, but what role does the Internet actually play in tourism? Before more money is placed into this new 'e-economy', it is important to study the effectiveness of the Internet as a marketing tool in tourism. In order to better address the concerns described above, this research accomplishes several tasks. First, the significance of researching Internet use within the tourism context is established. Specifically, theories and concepts from postmodernism, post-industrialism and post-structuralism are drawn upon as they frame this study. Second, this research explores motivation and decision making within tourism and how the Internet is used during stages of travel preparation, planning and activities. Third, this research explores tourist preferences for novelty and familiarity in three dimensions; travel services, social contact and destination choices, and examines how these are associated with Internet use. The general structure of tourism markets in relation to Internet use as well as novelty and familiarity preferences are also discussed. Three case studies are undertaken to examine these matters: winter tourists, summer tourists and cruise tourists. Novelty-seekers were found to be the most frequent group of Internet users, and also were the most likely to consult a wider variety of information sources when making travel-related decisions. Results also indicate that Internet use for travel varies according to seasonality and destination choices rather than primary activity.

Iris recognition systems capture an image from an individual's eye. The iris in the image is then segmented and normalized for feature extraction process. The performance of iris recognition systems highly depends on segmentation and normalization. For instance, even an effective feature extraction method would not be able to obtain useful information from an iris image that is not segmented or normalized properly. This thesis is to enhance the performance of segmentation and normalization processes in iris recognition systems to increase the overall accuracy. <br /><br /> The previous iris segmentation approaches assume that the boundary of pupil is a circle. However, according to our observation, circle cannot model this boundary accurately. To improve the quality of segmentation, a novel active contour is proposed to detect the irregular boundary of pupil. The method can successfully detect all the pupil boundaries in the CASIA database and increase the recognition accuracy. <br /><br /> Most previous normalization approaches employ polar coordinate system to transform iris. Transforming iris into polar coordinates requires a reference point as the polar origin. Since pupil and limbus are generally non-concentric, there are two natural choices, pupil center and limbus center. However, their performance differences have not been investigated so far. We also propose a reference point, which is the virtual center of a pupil with radius equal to zero. We refer this point as the linearly-guessed center. The experiments demonstrate that the linearly-guessed center provides much better recognition accuracy. <br /><br /> In addition to evaluating the pupil and limbus centers and proposing a new reference point for normalization, we reformulate the normalization problem as a minimization problem. The advantage of this formulation is that it is not restricted by the circular assumption used in the reference point approaches. The experimental results demonstrate that the proposed method performs better than the reference point approaches. <br /><br /> In addition, previous normalization approaches are based on transforming iris texture into a fixed-size rectangular block. In fact, the shape and size of normalized iris have not been investigated in details. In this thesis, we study the size parameter of traditional approaches and propose a dynamic normalization scheme, which transforms an iris based on radii of pupil and limbus. The experimental results demonstrate that the dynamic normalization scheme performs better than the previous approaches.

Medical imaging plays a vital role in the medical field because it is widely used in diseases diagnosis and treatment of patients. There are different modalities of medical imaging such as ultrasounds, x-rays, Computed Tomography (CT), Magnetic Resonance (MR), and Positron Emission Tomography (PET). Most of these modalities usually suffer from noise and other sampling artifacts. The diagnosis process in these modalities depends mainly on the interpretation of the radiologists. Consequently, the diagnosis is subjective as it is based on the radiologist experience. Medical image segmentation is an important process in the field of image processing. It has a significant role in many applications such as diagnosis, therapy planning, and advanced surgeries. There are many segmentation techniques to be applied on medical images. However, most of these techniques are still depending on the experts, especially for initializing the segmentation process. The artifacts of images can affect the segmentation output. In this thesis, we propose a new approach for automatic prostate segmentation of Trans-Rectal UltraSound (TRUS) images by dealing with the speckle not as noise but as informative signals. The new approach is an automation of the conventional region growing technique. The proposed approach overcomes the requirement of manually selecting a seed point for initializing the segmentation process. In addition, the proposed approach depends on unique features such as the intensity and the spatial Euclidean distance to overcome the effect of the speckle noise of the images. The experimental results of the proposed approach show that it is fast and accurate. Moreover, it performs well on the ultrasound images, which has the common problem of the speckle noise.

Outlining of the prostate boundary in ultrasound images is a very useful procedure performed and subsequently used by clinicians. The contribution of the resulting segmentation is twofold. First of all, the segmentation of the prostate glands can be used to analyze the size, geometry, and volume of the gland. Such analysis is useful as it is known that the former quantities used in conjunction with a PSA blood test can be used as an indicator of malignancy in the gland itself. The second purpose of accurate segmentation is for treatment planning purposes. In brachetherapy, commonly used to treat localized prostate cancer, the accurate location of the prostate must be found so that the radioactive seeds can be placed precisely in the malignant regions. Unfortunately, the current method of segmentation of ultrasound images is performed manually by expert radiologists. Due to the abundance of ultrasound data, the process of manual segmentation can be extremely time consuming and inefficient. A much more desirable way to perform the segmentation process is through automatic procedures, which should be able to accurately and efficiently extract the boundary of the prostate gland with minimal user intervention. This is the ultimate goal of the proposed approach. The proposed segmentation algorithm uses a probability distribution tracking framework to accurately and efficiently perform the task at hand. The basis for this methodology is to extract image and shape features from available manually segmented ultrasound images for which the actual prostate region is known. Then, the segmentation algorithm seeks a region in new ultrasound images whose features closely mirror the learned features of known prostate regions. Promising results were achieved using this method in a series of in silico and in vivo experiments.

Polycystic ovary syndrome (PCOS) is an endocrine abnormality with multiple diagnostic criteria due to its heterogenic manifestations. One of the diagnostic criterion includes analysis of ultrasound images of ovaries for the detection of number, size, and distribution of follicles within the ovary. This involves manual tracing of follicles on the ultrasound images to determine the presence of a polycystic ovary (PCO). A novel method that automates PCO morphology detection is described. Our algorithm involves automatic segmentation of follicles from ultrasound images, quantifying the attributes of the segmented follicles using stereology, storing follicle attributes as feature vectors, and finally classification of the feature vector into two categories. The classification categories are PCO morphology present and PCO morphology absent. An automatic PCO diagnostic tool would save considerable time spent on manual tracing of follicles and measuring the length and width of every follicle. Our procedure was able to achieve classification accuracy of 92.86% using a linear discriminant classifier. Our classifier will improve the rapidity and accuracy of PCOS diagnosis, and reduce the chance of the severe health implications that can arise from delayed diagnosis.