The maximum number (Nmax) of fish was recorded using baited underwater camera (BUC) techniques to study spatio-temporal changes of the fish community in localize areas. In the central North Sea (~100m depth) abundance models revealed that for both, whiting and flatfish, the Nmax was influenced by light level at the time of deployment. Nmax of hagfish was strongly related to current speed in a non-linear way with a predicted upper threshold (~11 cm.s-1) above which Nmax declined. Current speed and inter-annual variation had an important effect on haddock Nmax. Large predators that prompt avoidance mechanisms (e.g. large conger eel, marine mammals and fishing vessels) and bottom current speed >25 cm.s-1 can modify demersal fish behaviour depending on size and species and therefore have important effects on BUC-derived data. The BUC was applied for the first time to the monitoring of the Buzzard platform, a new oil and gas development in the central North Sea that host 12 demersal and benthic fish species. The benthic taxa, flatfish and hagfish, showed higher numbers closer to the platform whilst the most mobile species showed constant numbers (haddock) or highest numbers outside of the 500m of Exclusion Area (whiting). No significant changes in fish diversity were found between the areas surveyed before the platform construction. After the construction, the species richness and diversity were higher outside the 500m Exclusion Area surrounding the platform. Offshore sand banks, like oil platforms, can modify the composition of the local fish abundance. BUC and commercial trawl observations on the Jones Bank (75-150m depth, Celtic Sea) recorded 23 species, the highest richness, on top of the bank and 18 species on the slope and off-bank areas. Conger eel and Nephrops were both absent from the top of the bank site whereas haddock was only observed in the bank area.

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 Monte Carlo model has been developed using MCNP5 to simulate the Nucletron Ir-192 HDR source in order to investigate the influence of tissue heterogeneities on dose calculations compared to the dose in homogeneous water media, as it is typically calculated by brachytherapy Treatment Planning Systems (TPS). Validity of the simulation was verified in water medium in comparison with peer reviewed results using the dosimetric parameters recommended by AAPM, Task Group-43. The dose-rates in simulated prostate, bladder and rectum were compared to those obtained in the homogeneous water phantom. Based on the resulting dose differences, it is inferred that TPS algorithms for brachytherapy dose calculations overestimate the dose to tissues like prostate and bladder by up to 49%. A clinically relevant dose underestimation of 5.5% to the rectum was also found. We recommend that further investigation using actual patient CT data as input to the Monte Carlo simulation be performed. / by Ramsâes Herrera. / Thesis (M.S.)--Florida Atlantic University, 2012. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

Lau Wai Hung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 63-65). / Abstracts in English and Chinese. / abstract --- p.I / 摘要 --- p.II / acknowledgement --- p.III / table of contents --- p.IV / list of figures --- p.VI / Chapter CHAPTER 1 --- introduction --- p.1 / Chapter CHAPTER 2 --- literature review --- p.4 / Chapter 2.1 --- Traffic data collection methods --- p.4 / Chapter 2.2 --- Vision-based traffic monitoring techniques --- p.6 / Chapter 2.2.1 --- Vehicle tracking approaches --- p.7 / Chapter 2.2.2 --- Image processing techniques --- p.10 / Chapter CHAPTER 3 --- methodology --- p.15 / Chapter 3.1 --- Solution Concept --- p.16 / Chapter 3.2 --- System Framework --- p.18 / Chapter 3.2.1 --- Edge Detection Module --- p.20 / Chapter 3.2.2 --- Background Update Module --- p.22 / Chapter 3.2.3 --- Feature Extraction Modules --- p.25 / Chapter CHAPTER 4 --- experiments and evaluation --- p.41 / Chapter 4.1 --- Setup and Data Collection --- p.41 / Chapter 4.2 --- Evaluation Criteria --- p.42 / Chapter 4.3 --- Experimental Results --- p.44 / Chapter 4.3.1 --- Comparing overall accuracies --- p.44 / Chapter 4.3.2 --- Accuracies for different traffic conditions --- p.46 / Chapter 4.3.3 --- Comparing balanced sampling and random sampling --- p.48 / Chapter 4.3.4 --- Comparing day and night conditions --- p.50 / Chapter 4.3.5 --- Testing on time-series of images --- p.52 / Chapter CHAPTER 5 --- analysis --- p.54 / Chapter 5.1 --- Strengths and Weaknesses --- p.54 / Chapter 5.1.1 --- Sobel Edge Histogram --- p.54 / Chapter 5.1.2 --- Horizontal Line Detection --- p.55 / Chapter 5.1.3 --- Block Detection --- p.56 / Chapter 5.1.4 --- Combined Learning --- p.57 / Chapter 5.1.5 --- Overall Framework --- p.58 / Chapter 5.2 --- Future Research --- p.59 / Chapter 5.2.1 --- Static image based monitoring combined with other traffic monitoring approaches --- p.59 / Chapter 5.2.2 --- Horizontal Line Detection as tracked features of vehicles --- p.60 / Chapter 5.2.3 --- Application in aerial image-based system --- p.60 / Chapter CHAPTER 6 --- conclusion --- p.62 / bibliography --- p.63 / appendix a sobel edge detection --- p.66 / appendix b neural network setup --- p.67 / appendix c numerical results --- p.69

In this research three innovative registration systems were designed with the configurations of the mutual information and optimization technique: (1) mutual information combined with the downhill simplex method of optimization. (2) the derivative of mutual information combined with Quasi-Newton method. (3) mutual information combined with hybrid genetic algorithm (large-space random search) to avoid local maximum during the optimization. These automatic registration systems were evaluated with a variety of images, dimensions and voxel resolutions. Experiments demonstrate that registration system combined with mutual information and hybrid genetic algorithm can provide robust and accurate alignments to obtain a composite activation map for functional MRI analysis.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1977. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Ulick Oliver Malone. / M.S.

Thesis. 1975. Ph.D.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / Vita. / Includes bibliographical references. / Ph.D.

Thesis. 1976. M.S.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / Microfiche copy available in Archives and Engineering. / Includes bibliographical references. / by Sam M. Goldwasser. / M.S.

Encompassing with fields on engineering and medical image quality, this dissertation proposes a novel framework for diagnostic performance evaluation based on objective image-quality assessment, an important step in the development of new imaging devices, acquisitions, or image-processing techniques being used for clinicians and researchers. The objective of this dissertation is to develop computational modeling tools that allow comprehensive evaluation of task-based assessment including clinical interpretation of images regardless of image dimensionality. Because of advances in the development of medical imaging devices, several techniques have improved image quality where the format domain of the outcome images becomes multidimensional (e.g., 3D+time or 4D). To evaluate the performance of new imaging devices or to optimize various design parameters and algorithms, the quality measurement should be performed using an appropriate image-quality figure-of-merit (FOM). Classical FOM such as bias and variance, or mean-square error, have been broadly used in the past. Unfortunately, they do not reflect the fact that the average performance of the principal agent in medical decision-making is frequently a human observer, nor are they aware of the specific diagnostic task. The standard goal for image quality assessment is a task-based approach in which one evaluates human observer performance of a specified diagnostic task (e.g. detection of the presence of lesions). However, having a human observer performs the tasks is costly and time-consuming. To facilitate practical task-based assessment of image quality, a numerical observer is required as a surrogate for human observers. Previously, numerical observers for the detection task have been studied both in research and industry; however, little research effort has been devoted toward development of one utilized for multidimensional imaging studies (e.g., 4D). Limiting the numerical observer tools that accommodate all information embedded in a series of images, the performance assessment of a particular new technique that generates multidimensional data is complex and limited. Consequently, key questions remain unanswered about how much the image quality improved using these new multidimensional images on a specific clinical task. To address this gap, this dissertation proposes a new numerical-observer methodology to assess the improvement achieved from newly developed imaging technologies. This numerical observer approach can be generalized to exploit pertinent statistical information in multidimensional images and accurately predict the performance of a human observer over the complexity of the image domains. Part I of this dissertation aims to develop a numerical observer that accommodates multidimensional images to process correlated signal components and appropriately incorporate them into an absolute FOM. Part II of this dissertation aims to apply the model developed in Part I to selected clinical applications with multidimensional images including: 1) respiratory-gated positron emission tomography (PET) in lung cancer (3D+t), 2) kinetic parametric PET in head-and-neck cancer (3D+k), and 3) spectral computed tomography (CT) in atherosclerotic plaque (3D+e). The author compares the task-based performance of the proposed approach to that of conventional methods, evaluated based on a broadly-used signal-known-exactly /background-known-exactly paradigm, which is in the context of the specified properties of a target object (e.g., a lesion) on highly realistic and clinical backgrounds. A realistic target object is generated with specific properties and applied to a set of images to create pathological scenarios for the performance evaluation, e.g., lesions in the lungs or plaques in the artery. The regions of interest (ROIs) of the target objects are formed over an ensemble of data measurements under identical conditions and evaluated for the inclusion of useful information from different complex domains (i.e., 3D+t, 3D+k, 3D+e). This work provides an image-quality assessment metric with no dimensional limitation that could help substantially improve assessment of performance achieved from new developments in imaging that make use of high dimensional data.

No description available.