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The goal of this research is to improve the breast cancer screening process based on magnetic resonance imaging (MRI). In a typical MRI breast examination, a radiologist is responsible for visually examining the MR images acquired during the examination and identifying suspect tissues for biopsy. It is known that if multiple radiologists independently analyze the same examinations and we biopsy any lesion that any of our radiologists flagged as suspicious then the overall screening process becomes more sensitive but less specific. Unfortunately cost factors prohibit the use of multiple radiologists for the screening of every breast MR examination. It is thought that instead of having a second expert human radiologist to examine each set of images, that the act of second reading of the examination can be performed by a computer-aided detection and diagnosis system. The research presented in this thesis is focused on the development of a computer-aided detection and diagnosis system for breast cancer screening from dynamic contrast-enhanced magnetic resonance imaging examinations. This thesis presents new computational techniques in supervised learning, unsupervised learning and classifier visualization. The techniques have been applied to breast MR lesion data and have been shown to outperform existing methods yielding a computer aided detection and diagnosis system with a sensitivity of 89% and a specificity of 70%.

This thesis addresses the problem of size invariant shape recognition based on scale transformation within modulated competition neural layer. In this thesis I will present the advantages of applying neural networks in pattern recognition and study how the traditional automatic target recognition fails to recognise known patterns due to size change, cluttered backgrounds and distortion. Within the thesis we will also discuss possible ways to overcome size variance and how the combining of Selective Attention Adaptive Resonance Theory makes the system capable of recognising images with size changes, distortion and in complex backgrounds. The model is constructed based on neurophysiology experiments in vision systems. The Neural Circuit Simulation studies undertaken demonstrate the effectiveness of the proposed model in recognising 2D objects in many non-ideal visual conditions. Despite size differences from the stored memory image, difficult visual environments, including severe distortion, the simulation results indicate the model can recognise the shape stored in memory from the simulated shapes. / From the research presented in this thesis, it is concluded that the use of attentional mechanisms can enhance artificial vision systems to cope with difficult visual conditions. It is shown that feed-forward-feedback interactions with synaptic modulation are a versatile and powerful mechanism for performing many useful functions such as gain control, filtering and selective processing in neural network based vision systems. / Thesis (MEng(ComputerSystemsEng))--University of South Australia, 2004.

The construction of surface models from sensor data is an important part of perceptive robotics. When the sensor data are obtained from fixed sensors the problem of occlusion arises. To overcome occlusion, sensors may be mounted on a robot that moves the sensors over the surface. In this thesis the sensors are single–point range finders. The range finders provide a set of sensor points, that is, the surface points detected by the sensors. The sets of sensor points obtained during the robot’s motion are used to construct a surface model. The surface model is used in turn in the computation of the robot’s motion, so surface modelling is performed on–line, that is, the surface model is constructed incrementally from the sensor points as they are obtained. A planar polyhedral surface model is used that is amenable to incremental surface modelling. The surface model consists of a set of model segments, where a neighbour relation allows model segments to share edges. Also sets of adjacent shared edges may form corner vertices. Techniques are presented for incrementally updating the surface model using sets of sensor points. Various model segment operations are employed to do this: model segments may be merged, fissures in model segment perimeters are filled, and shared edges and corner vertices may be formed. Details of these model segment operations are presented. The robot’s control point is moved over the surface model at a fixed distance. This keeps the sensors around the control point within sensing range of the surface, and keeps the control point from colliding with the surface. The remainder of the robot body is kept from colliding with the surface by using redundant degrees–of–freedom. The goal of surface modelling and surface following is to model as much of the surface as possible. The incomplete parts of the surface model (non–shared edges) indicate where sections of surface that have not been exposed to the robot’s sensors lie. The direction of the robot’s motion is chosen such that the robot’s control point is directed to non–shared edges, and then over the unexposed surface near the edge. These techniques have been implemented and results are presented for a variety of simulated robots combined with real range sensor data.

The Recommendation Architecture (RA) model is a new connectionist approach simulating some aspects of the human brain. Application of the RA to a real world problem is a novel research problem and has not been previously addressed in literature. Research conducted with simulated data has shown much promise for the Recommendation Architecture model’s ability in pattern discovery and pattern recognition. This thesis investigates the application of the RA model for text mining where pattern discovery and recognition play an important role. The clustering system of the RA model is examined in detail and a formal notation for representing the fundamental components and algorithms is proposed for clarity of understanding. A software simulation of the clustering system of the RA model is built for empirical studies. In the argument that the RA model is applicable for text mining the following aspects of the model are examined. With its pattern recognition ability the clustering system of the RA is adapted for text classification and text organization. As the core of the RA model is concerned with pattern discovery or identification of associative similarities in input, it is also used to discover unsuspected relationships within the content of documents. How the RA model can be applied to the problems of pattern discovery in text and classification of text is addressed demonstrating results from a series of experiments. The difficulties in applying the RA model to real life data are described and several extensions to the RA model for optimal performance are proposed from the insights obtained from experiments. Furthermore, the RA model can be extended to provide user-friendly interpretation of results. This research shows that with the proposed extensions the RA model can be successfully applied to the problem of text mining to a large extent. Some limitations exist when the RA model is applied to very noisy data, which are also demonstrated here.

Thesis (M.S.)--University of Nevada, Reno, 2006. / "December, 2006." Includes bibliographical references (leaves 61-66). Online version available on the World Wide Web. Library also has microfilm. Ann Arbor, Mich. : ProQuest Information and Learning Company, [2006]. 1 microfilm reel ; 35 mm.

Thesis (Ph.D.)--Tufts University, 1998. / Adviser: Steven H. Levine. Submitted to the Dept. of Engineering Design. Includes bibliographical references (leaves 152-167). Access restricted to members of the Tufts University community. Also available via the World Wide Web;

Thesis (M.S.)--Rochester Institute of Technology, 1988. / Includes appendixes. Includes bibliographical references (leaves 86-87).