Submitted by JÚLIO HEBER SILVA (julioheber@yahoo.com.br) on 2017-05-11T19:49:56Z No. of bitstreams: 2 Dissertação - Paulo Freire Sobrinho - 2017.pdf: 10088390 bytes, checksum: 93cbaefd8f5c2dc974201729c2e859f7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2017-05-15T11:30:14Z (GMT) No. of bitstreams: 2 Dissertação - Paulo Freire Sobrinho - 2017.pdf: 10088390 bytes, checksum: 93cbaefd8f5c2dc974201729c2e859f7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-05-15T11:30:14Z (GMT). No. of bitstreams: 2 Dissertação - Paulo Freire Sobrinho - 2017.pdf: 10088390 bytes, checksum: 93cbaefd8f5c2dc974201729c2e859f7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-04-13 / Contextualization: Technologies like CAD systems, become ideal as a second opinion, that is, to work in partnership with the doctor. For example, PneumoCAD can be used to perform a diagnosis of absence or absence of pneumonia in children 1 to <5 years of age using X-ray radiographs. Problems: However, the use of PneumoCAD requires a region of interest, referring to the lungs, manually. Based on difficulty and difficulty, we sought a solution that was not found in any research, applied to radiographs, involving PneumoCAD, through the active model, having as a patient children between the ages of 1 and <5 years. Proposal: It is then proposed the use of the active model, associated with the technique developed and called nsAlterar in improvement to segmentation based on ns . Materials and Methods: Fifty-six “padrão ouro” radiographs were submitted to MATLAB, in 8 steps, through modified and improved algorithms, as well as implemented support tools, such as: As well as measures of similarity to investigate quantitatively , On an efficiency of all resources employed for the same purpose. Results: With this question, we obtained, after an analysis of the experiments, a taxon of hits for the right of spraying 75.61% and for the left one in 63.41%, in which nsAlterar promoted the improvement in the distributions, even if They were not segmented correctly, through approximations properly. Conclusions: Based on the active model associated with nsAlterar and other resources, it was possible to complement a functionality of the PneumoCAD system, through the use of segmentation in reais, thus contributing to a higher efficiency and better results. / Contextualização: As tecnologias como sistemas CAD, tornam-se ideais como segunda opinião, ou seja, para trabalhar em parceria com o médico. Por exemplo, o PneumoCAD pode ser utilizado para realização do diagnóstico de ausência ou não de pneumonia em crianças de 1 e < 5 anos de idade, através das radiografias de raios-X. Problemática: Entretanto, a utilização do PneumoCAD exige que uma região de interesse, referente aos pulmões, sejam determinadas manualmente. Baseado nesta exigência e dificuldade buscou-se alguma solução que não foi encontrada em nenhuma pesquisa, aplicada a radiografias, envolvendo o PneumoCAD, através do Active Shape Model, tendo como paciente crianças com idade entre 1 e < 5 anos. Proposta: É, então, proposto o uso do Active Shape Model, associado à técnica desenvolvida e denominada nsAlterar em melhora à segmentação baseada no ns . Materiais e Métodos: Foram submetidas, no MATLAB, 56 amostras de radiografias do “padrão-ouro”, em 8 etapas, através de algoritmos modificados e aperfei- çoados, além de ferramentas implementadas de apoio, como: para o treinamento a partir de exemplos; assim como as medidas de similaridades para buscar investigar, de maneira quantitativa, sobre a eficiência de todos os recursos empregados para o mesmo propósito. Resultados: Com isto, foi obtida, após a análise dos experimentos, a taxa de acertos para o pulmão direito em 75,61% e para o esquerdo em 63,41%, em que o nsAlterar promoveu o melhoramento nas distribuições, mesmo as que não foram segmentadas corretamente, através de aproximações de maneira adequada. Conclusões: A partir do Active Shape Model associado ao nsAlterar e demais recursos, foi possível complementar a funcionalidade do sistema PneumoCAD, através do uso da segmentação em situações reais, contribuindo, assim, para a obtenção de maior eficiência e de melhores resultados.

This thesis presents an investigation into Gyrodactylus species recognition, making use of machine learning classification and feature selection techniques, and explores image feature extraction to demonstrate proof of concept for an envisaged rapid, consistent and secure initial identification of pathogens by field workers and non-expert users. The design of the proposed cognitively inspired framework is able to provide confident discrimination recognition from its non-pathogenic congeners, which is sought in order to assist diagnostics during periods of a suspected outbreak. Accurate identification of pathogens is a key to their control in an aquaculture context and the monogenean worm genus Gyrodactylus provides an ideal test-bed for the selected techniques. In the proposed algorithm, the concept of classification using a single model is extended to include more than one model. In classifying multiple species of Gyrodactylus, experiments using 557 specimens of nine different species, two classifiers and three feature sets were performed. To combine these models, an ensemble based majority voting approach has been adopted. Experimental results with a database of Gyrodactylus species show the superior performance of the ensemble system. Comparison with single classification approaches indicates that the proposed framework produces a marked improvement in classification performance. The second contribution of this thesis is the exploration of image processing techniques. Active Shape Model (ASM) and Complex Network methods are applied to images of the attachment hooks of several species of Gyrodactylus to classify each species according to their true species type. ASM is used to provide landmark points to segment the contour of the image, while the Complex Network model is used to extract the information from the contour of an image. The current system aims to confidently classify species, which is notifiable pathogen of Atlantic salmon, to their true class with high degree of accuracy. Finally, some concluding remarks are made along with proposal for future work.

Osteoporosis, associated with reduced bone mineral density and structural degeneration, greatly increases the risk of fragility fracture. Magnetic resonance imaging (MRI) has been applied to central skeletal sites including the proximal femur due to its non-ionizing radiation. A major challenge of volumetric bone imaging of the hip is the selection of regions of interest (ROIs) for computation of regional bone measurements. To address this issue, an MRI-based active shape model (ASM) of the human proximal femur is applied to automatically generate ROIs. The challenge in developing the ASM for a complex three-dimensional (3-D) shape lies in determining a large number of anatomically consistent landmarks for a set of training shapes. This thesis proposes a new method of generating the proximal femur ASM, where two types of landmarks, namely fiducial and secondary landmarks, are used. The method consists of—(1) segmentation of the proximal femur bone volume, (2) smoothing the bone surface, (3) drawing fiducial landmark lines on training shapes, (4) drawing secondary landmarks on a reference shape, (5) landmark mesh generation on the reference shape using both fiducial and secondary landmarks, (6) generation of secondary landmarks on other training shapes using the correspondence of fiducial landmarks and an elastic deformation of the landmark mesh, (7) computation of the active shape model. A proximal femur ASM has been developed using hip MR scans of 45 post-menopausal women. The results of secondary landmark generation were visually satisfactory, and no topology violation or notable geometric distortion artifacts were observed. Performance of the method was examined in terms of shape representation errors in a leave-one-out test. The mean and standard deviation of leave-one-out shape representation errors were 0.34mm and 0.09mm respectively. The experimental results suggest that the framework of fiducial and secondary landmarks allows reliable computation of statistical shape models for complex 3-D anatomic structures.

Osteoporosis is a bone disease associated with fracture risk. Accurate assessments of fracture risk, guidelines to initiate preventive intervention, and monitoring treatment response are of paramount importance in public health. Clinically, osteoporosis is defined by low bone mineral density, which explains 65-75% of the variance in bone stiffness. The remaining variability is due to the cumulative and synergistic effects of various factors, including trabecular bone micro-architecture. Osteoporostic imaging is critically important in identifying fracture risks for planning of therapeutic intervention and monitoring response to treatments. In this work, quantitative analysis of trabecular bone micro-architecture using volumetric imaging techniques and computational biomechanical simulation through finite element modeling (FEM) are applied on in vivo imaging for various human studies. The ability of imaging methods in characterizing trabecular bone micro-architecture was experimentally examined using MRI and multi-row detector CT. They were found suitable for cross-sectional and longitudinal studies in monitoring changes of trabecular micro-architectural quality in clinical research. A framework which consists of robust segmentation of in vivo images and quality mesh generator, was constructed for FEM analysis. The framework was experimentally demonstrated effcient and effective to predict bone strength under limited spatial resolution. The ability of distinguishing bone strengths of different groups were evaluated on various human studies. And the relation between FEM and image-based micro-architectural measures was explored. Quantitative analysis supports the hypothesis that trabecular bone have distinct structural properties in different anatomic sites and the osteoporosis related change of the micro-architecture also varies. It highlight the importance of standardizing the definition of bone scan locations and the segmentation of such well-defined regions. A shape modeling method was proposed to solve the problem and its application in human proximal femur using MRI were presented. The method was compared with manual segmentation and found highly accurate. Together with tools developed for quantitative analysis, this work facilitates future researches of trabecular bone micro-architecture in different anatomic sites.

[pt] As expressões faciais transmitem muita informação sobre um indivíduo, tornando a capacidade de interpretá-las uma tarefa muito importante, com aplicações em diversas áreas, tais como Interação Homem Máquina, Jogos Digitais, storytelling interativo e TV/Cinema digital. Esta dissertação discute o processo de reconhecimento de emoções em tempo real usando ASM (Active Shape Model) e SVM (Support Vector Machine) e apresenta uma comparação entre duas formas comumente utilizadas na etapa de extração de atributos: faces neutra e média. Como não existe tal comparação na literatura, os resultados apresentados são valiosos para o desenvolvimento de aplicações envolvendo expressões de emoção em tempo real. O presente trabalho considera seis tipos de emoções: felicidade, tristeza, raiva, medo, surpresa e desgosto. / [en] The facial expressions provide a high amount of information about a person, making the ability to interpret them a high valued task that can be used in several fields of Informatics such as Human Machine Interface, Digital Games, interactive storytelling and digital TV/Cinema. This dissertation discusses the process of recognizing emotions in real time using ASM (Active Shape Model) and SVM (Support Vector Machine) and presents a comparison between two commonly used ways when extracting the attributes: neutral face and average. As such comparison can not be found in the literature, the results presented are valuable to the development of applications that deal with emotion expression in real time. The current study considers six types of emotions: happiness, sadness, anger, fear, surprise and disgust.

Optimal surface finding (OSF), a graph-based optimization approach to image segmentation, represents a powerful framework for medical image segmentation and analysis. In many applications, a pre-segmentation is required to enable OSF graph construction. Also, the cost function design is critical for the success of OSF. In this thesis, two issues in the context of OSF segmentation are addressed. First, a robust model-based segmentation method suitable for OSF initialization is introduced. Second, an OSF-based segmentation refinement approach is presented. For segmenting complex anatomical structures (e.g., lungs), a rough initial segmentation is required to apply an OSF-based approach. For this purpose, a novel robust active shape model (RASM) is presented. The RASM matching in combination with OSF is investigated in the context of segmenting lungs with large lung cancer masses in 3D CT scans. The robustness and effectiveness of this approach is demonstrated on 30 lung scans containing 20 normal lungs and 40 diseased lungs where conventional segmentation methods frequently fail to deliver usable results. The developed RASM approach is generally applicable and suitable for large organs/structures. While providing high levels of performance in most cases, OSF-based approaches may fail in a local region in the presence of pathology or other local challenges. A new (generic) interactive refinement approach for correcting local segmentation errors based on the OSF segmentation framework is proposed. Following the automated segmentation, the user can inspect the result and correct local or regional segmentation inaccuracies by (iteratively) providing clues regarding the location of the correct surface. This expert information is utilized to modify the previously calculated cost function, locally re-optimizing the underlying modified graph without a need to start the new optimization from scratch. For refinement, a hybrid desktop/virtual reality user interface based on stereoscopic visualization technology and advanced interaction techniques is utilized for efficient interaction with the segmentations (surfaces). The proposed generic interactive refinement method is adapted to three applications. First, two refinement tools for 3D lung segmentation are proposed, and the performance is assessed on 30 test cases from 18 CT lung scans. Second, in a feasibility study, the approach is expanded to 4D OSF-based lung segmentation refinement and an assessment of performance is provided. Finally, a dual-surface OSF-based intravascular ultrasound (IVUS) image segmentation framework is introduced, application specific segmentation refinement methods are developed, and an evaluation on 41 test cases is presented. As demonstrated by experiments, OSF-based segmentation refinement is a promising approach to address challenges in medical image segmentation.

This dissertation thesis deals with the automatic system for basic emotional facial expressions recognition from static images. Generally the system is divided into the three independent parts, which are linked together in some way. The first part deals with automatic face detection from color images. In this part they were proposed the face detector based on skin color and the methods for eyes and lips position localization from detected faces using color maps. A part of this is modified Viola-Jones face detector, which was even experimentally used for eyes detection. The both face detectors were tested on the Georgia Tech Face Database. Another part of the automatic system is features extraction process, which consists of two statistical methods and of one method based on image filtering using set of Gabor’s filters. For purposes of this thesis they were experimentally used some combinations of features extracted using these methods. The last part of the automatic system is mathematical classifier, which is represented by feed-forward neural network. The automatic system is utilized by adding an accurate particular facial features localization using active shape model. The whole automatic system was benchmarked on recognizing of basic emotional facial expressions using the Japanese Female Facial Expression database.

In the field of craniomaxillofacial (CMF) surgery, surgical planning is an important and necessary procedure due to the complex nature of the craniofacial skeleton. Computed tomography (CT) has brought about a revolution in virtual diagnosis, surgical planning and simulation, and evaluation of treatment outcomes. It provides high-quality 3D image and model of skull for Computer-aided surgical planning system (CSPS). During the planning process, one of the essential steps is to reestablish the dental occlusion. In the first project, a new approach is presented to automatically and efficiently reestablish dental occlusion. It includes two steps. The first step is to initially position the models based on dental curves and a point matching technique. The second step is to reposition the models to the final desired occlusion based on iterative surface-based minimum distance mapping with collision constraints. With linearization of rotation matrix, the alignment is modeled by solving quadratic programming. The simulation was completed on 12 sets of digital dental models. Two sets of dental models were partially edentulous, and another two sets have first premolar extractions for orthodontic treatment. Two validation methods were applied to the articulated models. The results show that using the proposed method, the dental models can be successfully articulated with a small degree of deviations from the occlusion achieved with the gold-standard method. Low contrast resolution in CBCT image has become its major limitation in building skull model. Intensive hand-segmentation is required to reconstruct the skull model. Thin bone images are particularly affected by this limitation. In the second project, a novel segmentation approach is presented based on wavelet active shape model (WASM) for a particular interest in the outer surface of the anterior wall of maxilla. 19 CBCT datasets are used to conduct two experiments. This model-based segmentation approach is validated and compared with three different segmentation approaches. The results show that the performance of this model-based segmentation approach is better than those of the other approaches. It can achieve 0.25 +/- 0.2mm of surface error distance from the ground truth of the bone surface. Field of view (FOV) can be reduced in order to reduce unnecessary radiation dose in CBCT. This ROI imaging is common in most of the dentomaxillofacial imaging and orthodontic practices. However, a truncation effect is created due to the truncation of projection images and becomes one of the limitation in CBCT. In the third project, a method for small region of interest (ROI) imaging and reconstruction of the image of ROI in CBCT and two experiments for measurement of dosage are presented. The first experiment shows at least 60% and 70% of radiation dose can be reduced. It also demonstrates that the image quality was still acceptable with little variation of gray by using the traditional truncation correction approach for ROI imaging. The second experiment demonstrates that the images reconstructed by CBCT reconstruction algorithms without truncation correction can be degraded to unacceptable image quality.

This thesis attempts to create an application to determine head rotation angle in video recorded from a security camera. The application consists of three parts: face detection, facial landmarks detection and determination of person's head rotation. The face detection has been implemented using Viola-Jones and HOG algorithms. Facial landmarks detection has been done using algorithm based on active shape model. Two methods to calculate the head rotation angles have been used: the first method works with anthropometric head features. The second method uses Perspective-n-Point algorithm to find the right rotation angles. Finally, all algorithms implemented have been tested and the proper parameters have been determined.