This thesis focuses on spatial resolution enhancement of magnetic resonance imaging (MRI). In particular, it addresses methods of performing such enhancement in the Fourier domain. After a brief review of Fourier theory, the thesis reviews the physics of the MRI acquisition process in order to introduce a mathematical model of the measured data. This model is later used to develop and analyze methods for resolution enhancement, or "super-resolution'', in MRI. We then examine strategies of performing super-resolution MRI (SRMRI). We begin by exploring strategies that use multiple data sets produced by spatial translations of the object being imaged, to add new information to the reconstruction process. This represents a more detailed mathematical examination of the author's Master's work at the University of Calgary. Using our model of the measured data developed earlier in the thesis, we describe how the acquisition strategy determines the efficacy of the SRMRI process that employs multiple data sets. The author then explores the self-similarity properties of MRI data in the Fourier domain as a means of performing spatial resolution enhancement. To this end, a fractal-based method over (complex-valued) Fourier Transforms of functions with compact spatial support, derived from a fractal transform in the spatial domain, is explored. It is shown that this method of "Iterated Fourier Transform Systems" (IFTS) can be tailored to perform frequency extrapolation, hence spatial resolution enhancement. The IFTS method, however, is limited in scope, as it assumes that a spatial function f(x) may be approximated by linear combinations of spatially-contracted and range-modified copies of the entire function. In order to improve the approximation, we borrow from traditional fractal image coding in the spatial domain, where subblocks of an image are approximated by other subblocks, and employ such a block-based strategy in the Fourier domain. An examination of the statistical properties of subblock approximation errors shows that, in general, Fourier data can be locally self-similar. Furthermore, we show that such a block-based self-similarity method is actually equivalent to a special case of the auto-regressive moving average (ARMA) modeling method. The thesis concludes with a chapter on possible future research directions in SRMRI.

Histologic analysis of tissue samples is often a critical step in the diagnosis of disease. However, this type of assessment is inherently subjective, and consequently a high degree of variability may occur between results produced by different pathologists. Histologic analysis is also a very time-consuming task for pathologists. Computer-based quantitative analysis of tissue samples shows promise for both reducing the subjectivity of traditional manual tissue assessments, as well as potentially reducing the time required to analyze each sample. <p>The objective of this thesis project was to investigate image processing techniques and to develop software which could be used as a diagnostic aid in pathology assessments of cattle lung tissue samples. The software examines digital images of tissue samples, identifying and highlighting the presence of a set of features that indicate disease, and that can be used to distinguish various pulmonary diseases from one another. The output of the software is a series of segmented images with relevant disease indicators highlighted, and measurements quantifying the occurrence of these features within the tissue samples. Results of the software analysis of a set of 50 cattle lung tissue samples were compared to the detailed manual analysis of these samples by a pathology expert.<p>The combination of image analysis techniques implemented in the thesis software shows potential. Detection of each of the disease indicators is successful to some extent, and in some cases the analysis results are extremely good. There is a large difference in accuracy rates for identification of the set of disease indicators, however, with sensitivity values ranging from a high of 94.8% to a low of 22.6%. This wide variation in result scores is partially due to limitations of the methodology used to determine accuracy.

We present a novel data hiding method for compressed images. The method is designed to minimize the quality loss associated with data embedding into a JPEG image. The described technique uses the objective criterion such as the mean square error and the human visual system based criterion such as the Just Noticable Distortion metric for distortion minimization. The hiding method is designed under the restrictions of the JPEG compression standard to develop new image applications without any modifications or additions to the existing standard. An application example is presented in the thesis. The performance of the technique is examined at different image sizes and resolutions. The cost of hiding in terms of file length extension is examined. Some subjective experiments to determine the zero-perceived distortion hiding capacity are made. An application illustrating the usage of the technique is given. The described application embeds check-bits into JPEG images to facilitate the verification of the sender identity and the authenticity of the transmitted image. In this thesis, we give a list of requirements on the data hiding methods to implement standard compliant applications; design a provably good hiding method operating under these requirements; determine the critical performance points of the method and propose an application based on the method. We have performed some additional research to determine how our system works with high resolution images and existing other well-known algorithms for information hiding. The experiments on high-resolution images have shown that there exists a large embedding capacity for the high resolution images in spite of a loss of embedding density. The performance comparison experiments have shown that the spread spectrum technique offers a competitive but less efficient distortion performance.

The main purpose of this thesis is to detect pedestrian crossing by static and dynamic image processing. This technique can help the blind and the disabled people to find the pedestrian crossing and walk through it safely. Until now there is no research about detecting pedestrian crossing in Taiwan. Therefore, this article applies the algorithm of Bipolarity feature in an image-based technique. In this thesis, the Bipolarity is regarded as the main feature in detecting pedestrian crossing. At first, it uses the features of pedestrian crossing, the black road surface is painted with constant-width periodic white stripes. After computation, the analysis and comparison in an image of intensity distribution is obtained. And the background will be eliminated. Secondly, Connected Component Labeling is used to extract the most similar region, and the marked region will be detected by the image. Finally, this thesis will detect whether the crossing exist or not in the marked region and measure the length of crossing. In dynamic model, the real-time image processing technique combines with wheelchair robot in order to walk through pedestrian crossing automatically, and image processing technique provides real-time offset and angle of displacement for the wheelchair robot to control and reach the destination. In this thesis, the image processing is in PC-base, and it receives the information by using a digital camera to record the real scene of pedestrian crossing. Keywords:pedestrian crossing , image processing, wheelchair robot

In recent years, computer tomography (CT) has become an increasingly important tool in the clinical diagnosis, mainly because of the advent of fast scanning techniques and high spatial resolution of the vision hardware. In addition to the detailed information of morphology, functional CT also gives the physiologic information, such as perfusion. It can help doctors to make better decision. Our goal in this paper is to evaluate lung perfusion by comparing pre and post contrast-enhanced CT images. After the contrast agent is injected, it flows with blood stream and causes the temporal changes in CT values. Therefore, we can quantize perfusion values from the changes of CT values between pre and post contrast-enhanced CT images. Then guided by color -coded maps, a quantitative analysis for the assessment of lung perfusion can be performed. As a result, it is easier for observer to determinate the lung perfusion distribution. Moreover, we can use color - coded images to visualize pulmonary embolism and monitor therapeutic efficacy.

The morphological image processing can modify the shapes of objects very efficiently by structure elements. Thus, the morphology processing has recently been applied to industry auto-inspection and medical image processing successfully. In this thesis, we incestigate the efficient processing of morphological image processing by two approaches: quadtree approach and paralell approach. By the quadtree decomposition, any binary image can be decomposed into black and white square blocks with some fixed size of power of 2. Thus, dilation of the whole image can be accomplished by dilating individual decomposed square blocks. On the other hand, any binary image can be presented by bit per pixel basis. Thus, we can exploit the parallel on a personal computer to speed up the set oriented morphological image processing. Experiments have revealed that both approach are much faster than the direct method. The quadtree approach are most advantageous for large structure elements. Whereas, the parralel approach are the fastest for the usual applications.

¡@¡@Morphological image processing technique has been well applied to many image processing areas. However, its long computation time usually can¡¦t be accepted when run on a general purpose sequential computer. Instead of conventional image data representation, some special data structures have led to the development of efficient algorithms. ¡@¡@The quadtree data structure has been well applied to the field of computer vision such as image segmentation and compression. The quadtree with its hierarchical data structure are advantageous due to its ability to focus on the interesting subsets of the data. Thus, the quadtree data structure are particularly convenient for set operations. Therefore, the computation for morphology processing will be facilitated by using the quadtree data structure since the set operations are the basics of the morphology processing. ¡@¡@Although many fast morphological image processing methods have been presented, there is still a need for developing a distributed computing architecture for morphological processing. We propose a scheme direct connection for client to client to improve the data transfer efficiency. Also, the image data is compressed by the quadtree for the transmission efficiency. Due to the efficiency of the network connection and data compression, we have established an efficient web-based distributed work station for morphological image processing.

Morphological image processing is especially useful in the applications of medical image processing, pattern recognition, and industry auto-inspection. Special hardware for morphological image processing are very expensive. On the other hand, the speed of software are too slow. The purpose of this paper is to speed up the software computations of morphological image processing by parallel processing on Pentium machine. The morphological operation is similar to digital convolution. We can realize our parallel morphological operation on the Pentium machine by two different methods. They are output-decomposition and input-decomposition methods, similar to the procedure of overlap-and-save and overlap-and-add respectively. The above methods implemented on Pentium machine are proved very efficient with 64-bits parallelism. Our experimental results demonstrated they are twice faster than the 32-bits parallelism method. In addition to the simulation and the real time experiments, a set of theoretical formulas are derived to analyze our methods and are checking the actual measured time quite well.

Due to the Columbia Space Shuttle Accident of February 2003, the Columbia Accident Investigation Board determined the need for an on-orbit inspection system for the Thermal Protection System that accurately determines damage depth to 0.25". NASA contracted the Spacecraft Technology Center in College Station, Texas, for a proof-of-concept photogrammetric system. This system involves a high quality digital camera placed on the International Space Station, capable of taking high fidelity images of the orbiter as it rotates through the Rendezvous Pitch Maneuver. Due to the pitch rotation, the images are tilted at different angles. The tilt causes the damage to exhibit parallax between multiple images. The tilted images are therefore registered to the near-vertical images using visually striking features on the undamaged surface of the Thermal Protection System that appear in multiple images taken at different tilt angles. The images become relatively oriented after registration, and features in one image are ensured to lie on the epipolar line in the other images. Features that do not lie on the undamaged surface, however, are shifted in the tilted images. These pixels are matched to the near-vertical image using a sliding-window area-matching approach. The windows are matched using a least-squares error method. The change in location for a pixel in a tilted image from its expected location on the undamaged surface is called the pixel disparity. This disparity is linearly scaled using the tilt angle and the pixel sampling to determine the depth of the damage at that pixel location. The algorithm is tested on a set of damaged tiles at the Johnson Space Center in Houston and the photogrammetric damage depth is then compared to a set of truth data provided by NASA. The photogrammetric method shows promise, with the 0.25" error limit being exceeded in only a few pixel locations. Once the camera properties are fully known from calibration, this systematic error should be reduced.

On-line gap measurement techniques for steel mill non-contacting conveyance system, which can supply accurate, rapid and high-sampling rate gap measurements, have been proposed. To realize the entire process, by considering the operational environment in a steel mill and combining with those available system dimension measurement instruments, an image-based scheme with proper image processing and parameter calibration process has been developed. The possible sources that affect the air-gap detection accuracies have also been thoroughly investigated, and a comprehensive measurement database and a recursive modification technique have been successfully established. In order to achieve stable control for site implementation, an integrated optical inspection system which combined with the high-speed rate line-scan camera has been designed. From the experimental results, the proposed system can both provide accurate gap values at the static state, and offer stable control operations at the dynamic state. It is believed that the proposed scheme provide innovated guidance for the related conveyance applications in the steel mill.