A Prototype Computational Phantom to Create Digital Images for Research and Training in Diagnostic Radiology

Research in the processing, compression, transmission, and interpretation of digital radiographic images require the testing and evaluation of a wide variety of images, varying both in format and in spatial resolution. If receiver operating characteristic (ROC) analysis or a related method is used to evaluate the performance of observers using novel vs. conventional displays, large numbers of test images containing known abnormalities are required. This report describes a convenient, inexpensive, and reproducible source of test images, having any desired resolution and containing precisely defined abnormalities of unlimited subtlety. The images are generated by computing x-ray transmission through mathematically defined, three dimensional masses according to Beer\u27s Law. A procedure is presented for generating computer simulated chest radiographs and mammograms, which can contain various classes of abnormalities, including tumors, infiltrates, cavities, pleural effusions, cardiac chamber enlargement, and soft tissue calcifications. Test images can be created from simple computational models of superimposed spherical densities. The approach provides a flexible, inexpensive, easy-to-use research tool for investigators exploring digital techniques in diagnostic radiology. Such simulation software may also be of benefit as a training tool, when employed to generate numerous test images containing subtle abnormalities for programmed instruction and testing

Sistema de inspeção visual de placas de circuito impresso para linhas de produção em pequenas séries em um contexto multiagentes

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2015.A produção em pequenas séries (PPS) vem se destacando e aumentando sua representatividade no cenário econômico, principalmente quando o assunto é tecnologia. Atualmente, percebe-se que na produção de placas de circuito impresso (PCI), há uma necessidade de produzir de maneira personalizada e eficiente, logo, há um esforço da comunidade científica e industrial em aprimorar técnicas de processamento de imagens para a inspeção de PCI. Este trabalho inclui o estado da arte da inspeção óptica automática de PCI, não necessariamente aplicada a PPS. As técnicas utilizadas neste projeto visam a formação de um sistema para a inspeção de componentes do tipo SMD em uma PPS, garantindo uma qualidade de produção satisfatória, com a finalidade de reduzir o retrabalho. Com isso, foi proposto um sistema de inspeção baseado em características relacionadas ao contorno, posicionamento e histograma dos componentes. Este sistema é composto pelas seguintes três etapas: pré-processamento de imagens, extração de características e avaliação de componentes. A máquina de inspeção utilizada neste projeto está inserida no contexto de cooperação entre máquinas, com o objetivo de constituir uma fábrica totalmente autônoma, coordenada por um sistema multiagente. Desta forma, foi desenvolvido um método de interação entre a máquina de inspeção e um agente que a representa. Um software de inspeção foi implementado utilizando as arquiteturas e estruturas descritas ao longo desta dissertação. Os resultados obtidos pelos experimentos mostram-se adequados à inspeção de componentes SMD em uma PPS, pois apontam uma taxa de acerto acima de 89% ao utilizar componentes reais..Abstract : Small series production (SSP) has been higlighting and increasing its share in the economic scenario, especially when it comes to technology. Currently, it is seen that in printed circuit boards (PCB) production, there is a need to produce in a customized and efficient way, so an effort of the scientific and industrial community is present, to improve the image processing techniques for PCB inspection. It is present in this work the state of the art for automatic optical inspection in a PCB, not necessarily applied to SSP. The techniques used in this project aim the formation of a system to inspect SMD components in a SSP, ensuring a satisfactory production quality, with the purpose of reduce the rework. Therefore, an inspection system based on characteristics related to shape, positioning and histogram of components has been proposed. This system is compound of the following three steps: pre-processing of images, feature extraction and evaluation components. The inspection machine used in this project is inserted in the context of cooperation among machines in order to provide a fully autonomous factory, coordinated by a multiagent system. Thus, a interaction method between the inspection machine and a agent was developed. An inspection software was implemented using the architecture and structures described throughout this dissertation. The results obtained by experiments show that it is suitable for inspection of SMD components in a SSP, showing a success rate above 89% when using actual components

Recurrent neural network for optimization with application to computer vision.

by Cheung Kwok-wai.Thesis (M.Phil.)--Chinese University of Hong Kong, 1993.Includes bibliographical references (leaves [146-154]).Chapter Chapter 1 --- IntroductionChapter 1.1 --- Programmed computing vs. neurocomputing --- p.1-1Chapter 1.2 --- Development of neural networks - feedforward and feedback models --- p.1-2Chapter 1.3 --- State of art of applying recurrent neural network towards computer vision problem --- p.1-3Chapter 1.4 --- Objective of the Research --- p.1-6Chapter 1.5 --- Plan of the thesis --- p.1-7Chapter Chapter 2 --- BackgroundChapter 2.1 --- Short history on development of Hopfield-like neural network --- p.2-1Chapter 2.2 --- Hopfield network model --- p.2-3Chapter 2.2.1 --- Neuron's transfer function --- p.2-3Chapter 2.2.2 --- Updating sequence --- p.2-6Chapter 2.3 --- Hopfield energy function and network convergence properties --- p.2-1Chapter 2.4 --- Generalized Hopfield network --- p.2-13Chapter 2.4.1 --- Network order and generalized Hopfield network --- p.2-13Chapter 2.4.2 --- Associated energy function and network convergence property --- p.2-13Chapter 2.4.3 --- Hardware implementation consideration --- p.2-15Chapter Chapter 3 --- Recurrent neural network for optimizationChapter 3.1 --- Mapping to Neural Network formulation --- p.3-1Chapter 3.2 --- Network stability verse Self-reinforcement --- p.3-5Chapter 3.2.1 --- Quadratic problem and Hopfield network --- p.3-6Chapter 3.2.2 --- Higher-order case and reshaping strategy --- p.3-8Chapter 3.2.3 --- Numerical Example --- p.3-10Chapter 3.3 --- Local minimum limitation and existing solutions in the literature --- p.3-12Chapter 3.3.1 --- Simulated Annealing --- p.3-13Chapter 3.3.2 --- Mean Field Annealing --- p.3-15Chapter 3.3.3 --- Adaptively changing neural network --- p.3-16Chapter 3.3.4 --- Correcting Current Method --- p.3-16Chapter 3.4 --- Conclusions --- p.3-17Chapter Chapter 4 --- A Novel Neural Network for Global Optimization - Tunneling NetworkChapter 4.1 --- Tunneling Algorithm --- p.4-1Chapter 4.1.1 --- Description of Tunneling Algorithm --- p.4-1Chapter 4.1.2 --- Tunneling Phase --- p.4-2Chapter 4.2 --- A Neural Network with tunneling capability Tunneling network --- p.4-8Chapter 4.2.1 --- Network Specifications --- p.4-8Chapter 4.2.2 --- Tunneling function for Hopfield network and the corresponding updating rule --- p.4-9Chapter 4.3 --- Tunneling network stability and global convergence property --- p.4-12Chapter 4.3.1 --- Tunneling network stability --- p.4-12Chapter 4.3.2 --- Global convergence property --- p.4-15Chapter 4.3.2.1 --- Markov chain model for Hopfield network --- p.4-15Chapter 4.3.2.2 --- Classification of the Hopfield markov chain --- p.4-16Chapter 4.3.2.3 --- Markov chain model for tunneling network and its convergence towards global minimum --- p.4-18Chapter 4.3.3 --- Variation of pole strength and its effect --- p.4-20Chapter 4.3.3.1 --- Energy Profile analysis --- p.4-21Chapter 4.3.3.2 --- Size of attractive basin and pole strength required --- p.4-24Chapter 4.3.3.3 --- A new type of pole eases the implementation problem --- p.4-30Chapter 4.4 --- Simulation Results and Performance comparison --- p.4-31Chapter 4.4.1 --- Simulation Experiments --- p.4-32Chapter 4.4.2 --- Simulation Results and Discussions --- p.4-37Chapter 4.4.2.1 --- Comparisons on optimal path obtained and the convergence rate --- p.4-37Chapter 4.4.2.2 --- On decomposition of Tunneling network --- p.4-38Chapter 4.5 --- Suggested hardware implementation of Tunneling network --- p.4-48Chapter 4.5.1 --- Tunneling network hardware implementation --- p.4-48Chapter 4.5.2 --- Alternative implementation theory --- p.4-52Chapter 4.6 --- Conclusions --- p.4-54Chapter Chapter 5 --- Recurrent Neural Network for Gaussian FilteringChapter 5.1 --- Introduction --- p.5-1Chapter 5.1.1 --- Silicon Retina --- p.5-3Chapter 5.1.2 --- An Active Resistor Network for Gaussian Filtering of Image --- p.5-5Chapter 5.1.3 --- Motivations of using recurrent neural network --- p.5-7Chapter 5.1.4 --- Difference between the active resistor network model and recurrent neural network model for gaussian filtering --- p.5-8Chapter 5.2 --- From Problem formulation to Neural Network formulation --- p.5-9Chapter 5.2.1 --- One Dimensional Case --- p.5-9Chapter 5.2.2 --- Two Dimensional Case --- p.5-13Chapter 5.3 --- Simulation Results and Discussions --- p.5-14Chapter 5.3.1 --- Spatial impulse response of the 1-D network --- p.5-14Chapter 5.3.2 --- Filtering property of the 1-D network --- p.5-14Chapter 5.3.3 --- Spatial impulse response of the 2-D network and some filtering results --- p.5-15Chapter 5.4 --- Conclusions --- p.5-16Chapter Chapter 6 --- Recurrent Neural Network for Boundary DetectionChapter 6.1 --- Introduction --- p.6-1Chapter 6.2 --- From Problem formulation to Neural Network formulation --- p.6-3Chapter 6.2.1 --- Problem Formulation --- p.6-3Chapter 6.2.2 --- Recurrent Neural Network Model used --- p.6-4Chapter 6.2.3 --- Neural Network formulation --- p.6-5Chapter 6.3 --- Simulation Results and Discussions --- p.6-7Chapter 6.3.1 --- Feasibility study and Performance comparison --- p.6-7Chapter 6.3.2 --- Smoothing and Boundary Detection --- p.6-9Chapter 6.3.3 --- Convergence improvement by network decomposition --- p.6-10Chapter 6.3.4 --- Hardware implementation consideration --- p.6-10Chapter 6.4 --- Conclusions --- p.6-11Chapter Chapter 7 --- Conclusions and Future ResearchesChapter 7.1 --- Contributions and Conclusions --- p.7-1Chapter 7.2 --- Limitations and Suggested Future Researches --- p.7-3References --- p.R-lAppendix I The assignment of the boundary connection of 2-D recurrent neural network for gaussian filtering --- p.Al-1Appendix II Formula for connection weight assignment of 2-D recurrent neural network for gaussian filtering and the proof on symmetric property --- p.A2-1Appendix III Details on reshaping strategy --- p.A3-

Edge detection using neural network arbitration

A human observer is able to recognise and describe most parts of an object by its contour, if this is properly traced and reflects the shape of the object itself. With a machine vision system this recognition task has been approached using a similar technique. This prompted the development of many diverse edge detection algorithms. The work described in this thesis is based on the visual observation that edge maps produced by different algorithms, as the image degrades. Display different properties of the original image. Our proposed objective is to try and improve the edge map through the arbitration between edge maps produced by diverse (in nature, approach and performance) edge detection algorithms. As image processing tools are repetitively applied to similar images we believe the objective can be achieved by a learning process based on sample images. It is shown that such an approach is feasible, using an artificial neural network to perform the arbitration. This is taught from sets extracted from sample images. The arbitration system is implemented upon a parallel processing platform. The performance of the system is presented through examples of diverse types of image. Comparisons with a neural network edge detector (also developed within this thesis) and conventional edge detectors show that the proposed system presents significant advantages

Edge detection using neural network arbitration

A human observer is able to recognise and describe most parts of an object by its contour, if this is properly traced and reflects the shape of the object itself. With a machine vision system this recognition task has been approached using a similar technique. This prompted the development of many diverse edge detection algorithms. The work described in this thesis is based on the visual observation that edge maps produced by different algorithms, as the image degrades. Display different properties of the original image. Our proposed objective is to try and improve the edge map through the arbitration between edge maps produced by diverse (in nature, approach and performance) edge detection algorithms. As image processing tools are repetitively applied to similar images we believe the objective can be achieved by a learning process based on sample images. It is shown that such an approach is feasible, using an artificial neural network to perform the arbitration. This is taught from sets extracted from sample images. The arbitration system is implemented upon a parallel processing platform. The performance of the system is presented through examples of diverse types of image. Comparisons with a neural network edge detector (also developed within this thesis) and conventional edge detectors show that the proposed system presents significant advantages