This dissertation presents image processing methods designed to enhance images obtained by angiography, and applied image analysis methods to quantify the vascular diameter. An iterative, non-linear enhancement technique is described for enhancing the edges of blood vessels in unsubtracted angiographic images. The technique uses a median filter and the point spread function of the imaging system to increase the resolution of the image while keeping down noise. Evaluation of the images by radiologists showed that they preferred the processed images over the unprocessed images. Also described is a heuristic, recursive, vessel tracking algorithm. The tracker is intended for use with digital subtraction angiography images. The vascular system is characterized by a tree data structure. Tree structures are inherently recursive structures and thus recursive programming languages are ideally suited for building and describing them. The tracker uses a window to follow the centerlines of the vessels and stores parameters describing the vessels in nodes of a binary tree. Branching of the vascular tree is handled automatically. A least squares fit of a cylindrical model to intensity profiles of the vessel is used to estimate vessel diameter and other parameters. The tracker is able to successfully track vessels with signal-to-noise ratios down to about 4. Several criteria are applied to distinguish between vessel and noise. The relative accuracy of the diameter estimate is about 3% to 8% for a signal-to-noise ratio of 10; the absolute accuracy depends on the magnification (mm per sample). For the clinically significant case of a 25% stenosis (narrowing of the vessel), the absolute error in estimating the percent stenosis is 3.7% of the normal diameter and the relative error is 14.8%. This relative error of 14.8% is a substantial improvement over relative errors of 30% to 70% produced by other methods.

This dissertation presents a contrast-enhancement algorithm called Artifact-Suppressed Adaptive Histogram Equalization (ASAHE). This algorithm was developed as part of a larger effort to replace the film radiographs currently used in radiology departments with digital images. Among the expected benefits of digital radiology are improved image management and greater diagnostic accuracy. Film radiographs record X-ray transmission data at high spatial resolution, and a wide dynamic range of signal. Current digital radiography systems record an image at reduced spatial resolution and with coarse sampling of the available dynamic range. These reductions have a negative impact on diagnostic accuracy. The contrast-enhancement algorithm presented in this dissertation is designed to boost diagnostic accuracy of radiologists using digital images. The ASAHE algorithm is an extension of an earlier technique called Adaptive Histogram Equalization (AHE). The AHE algorithm is unsuitable for chest radiographs because it over-enhances noise, and introduces boundary artifacts. The modifications incorporated in ASAHE suppress the artifacts and allow processing of chest radiographs. This dissertation describes the psychophysical methods used to evaluate the effects of processing algorithms on human observer performance. An experiment conducted with anthropomorphic phantoms and simulated nodules showed the ASAHE algorithm to be superior for human detection of nodules when compared to a computed radiography system's algorithm that is in current use. An experiment conducted using clinical images demonstrating pneumothoraces (partial lung collapse) indicated no difference in human observer accuracy when ASAHE images were compared to computed radiography images, but greater ease of diagnosis when ASAHE images were used. These results provide evidence to suggest that Artifact-Suppressed Adaptive Histogram Equalization can be effective in increasing diagnostic accuracy and efficiency.

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The structure from motion (SfM) problem is that of determining 3-dimensional (3D) information of a scene from sequences of 2-dimensional (2D) images [59]. This information consists of object shape and motion and relative camera motion. In general, objects may undergo complex non-rigid motion and may be occluded by other objects or themselves. These aspects make the general SfM problem under-constrained and the solution subject to missing or incomplete data.

Approved for public release; distribution is unlimited. / Inverse Synthetic Aperture Radar (ISAR) provides full range detection and classification of sea and air based targets through two-dimensional range-Doppler imaging. The Naval Postgraduate School has developed a custom integrated circuit that can simulate false ISAR images in order to fool enemy ISAR platforms. To validate specific hardware choices within this design, this thesis explores the effect on image quality of an overflow occurring within the final 16-bit summation adder of this circuit. Three solutions to the problem of overflows are presented and analyzed. The logical extension of ISAR development, that of three-dimensional target imaging, is next presented through the discussion of 3D monopulse radar, 3D interferometric ISAR, and a 3D, three receiver ISAR. The relative strengths of each approach are compared, along with both MATLAB and X3D software models created for one specific 3D ISAR implementation. Through the superposition of 2D ISAR images it is shown how 3D ISAR images may be created. Moreover, emphasis is placed on using this knowledge to both enhance current 2D ISAR techniques and to modify the false-target chip to handle 3D ISAR return signals. The thesis concludes with a study of Non-Uniform Rational B-Splines, through which the X3D software model was created. / Ensign, United States Naval Reserve

A thesis submitted to the Faculty of Engineering, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor of Philosophy Johannesburg, October 1997 / This thesis develops and investigates signal processing models that are useful both for interpreting and implementing certain types of Model Predictive Control. Two types of Model Predictive Control are investigated, namely, techniques based on Internal Model Control and Long Range Predictive Control. [Abbreviated abstract. Open document to view full version].

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science. Johannesburg, 2016. / The l1-norm solution to an under-determined system of linear equations y = Ax is the sparsest solution to the system. In digital signal processing this mathematical problem is known as compressive sensing. Compressive sensing provides a mathematical framework for sampling and reconstructing an analogue signal at a rate far lower than the rate provided by the standard information theory. The reconstruction from few samples is possible using non-linear optimization algorithms provided that the signal is sparse and the sensing matrix in incoherent. The major algorithmic challenge in compressive sensing is to efficiently and effectively find sparse solutions from minimal measurements. General purpose optimization algorithms are not suitable for solving non-differentiable l1-minimization problem. In this dissertation, we survey the major practical algorithms for nding l1-norm solution of under-determined linear system of equations. Specific attention is paid to computational issues, in which individual methods tends to perform well. We propose a hybrid algorithm that combines complementary strengths of the fixed-point method and the interior-point method. The strong feature of the xed-point method is its speed, while the strength of the interior-point method is accuracy. The hybrid algorithm combine the two methods in a probabilistic manner. The algorithm tends to prioritise a method that is efficient and robust. The computational performance of the hybrid algorithm is tested on simple signal reconstruction problems. The hybrid algorithm is shown to produce similar recoverability of sparse solution as that of the xed-point method and the interior-point method. Furthermore the proposed hybrid algorithm is comparative in terms of speed and accuracy with existing methods. / LG2017

The Intermediate Frequency Acoustic Modem (IFAM), developed by Dr. Beaujean, is designed to transmit the command-and-control messages from the top-side to the wet-side unit in ports and very shallow waters. This research presents the design of the turbo coding scheme and its implementation in the IFAM modem with the purpose of meeting a strict requirement for the IFAM error rate performance. To simulate the coded IFAM, a channel simulator is developed. It is basically a multi-tap filter whose parameters are set depending on the channel geometry and system specifics. The simulation results show that the turbo code is able to correct 89% of the messages received with errors in the hostile channel conditions. The Bose-Chadhuri-Hocquenghem (BCH) coding scheme corrects less that 15% of these messages. The other simulation results obtained for the system operation in different shallow water settings are presented. / by Milutin Pajovic. / Thesis (M.S.C.S.)--Florida Atlantic University, 2009. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.

University of Macau / Faculty of Health Sciences