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This research presents a procedure for interactive segmentation and automatic tracking of moving objects in a video sequence. The user outlines the region of interest (ROI) in the initial frame; the procedure builds a refined mask of the dominant object within the ROI. The refined mask is used to model a spline template of the object to be tracked. The tracking algorithm then employs a motion model to track the template through a sequence of frames and gathers the 3D affine motion parameters of the object from each frame. The extracted template is compared with a previously stored library of 3D shapes to determine the closest 3D object. If the extracted template is completely new, it is used to model a new 3D object which is added to the library. To recreate the motion, the motion parameters are applied to the 3D object in a virtual environment. The procedure described here can be applied to industrial problems such as traffic management and material flow congestion analysis.

This research presents a procedure for interactive segmentation and automatic tracking of moving objects in a video sequence. The user outlines the region of interest (ROI) in the initial frame; the procedure builds a refined mask of the dominant object within the ROI. The refined mask is used to model a spline template of the object to be tracked. The tracking algorithm then employs a motion model to track the template through a sequence of frames and gathers the 3D affine motion parameters of the object from each frame. The extracted template is compared with a previously stored library of 3D shapes to determine the closest 3D object. If the extracted template is completely new, it is used to model a new 3D object which is added to the library. To recreate the motion, the motion parameters are applied to the 3D object in a virtual environment. The procedure described here can be applied to industrial problems such as traffic management and material flow congestion analysis.

This thesis tries to answer if a semi-automatic tool can speed up the process of segmenting tumors to find the area of a slice in the tumor or the volume of the entire tumor. A few different 2D semi-automatic tools were considered. The final choice was to implement live-wire. The implemented live-wire was evaluated and improved upon with hands-on testing from developers. Two methods were found for extending live-wire to 3D bodies. The first method was to interpolate the seed points and create new contours using the new seed points. The second method was to let the user segment contours in two orthogonal projections. The intersections between those contours and planes in the third orthogonal projection were then used to create automatic contours in this third projection. Both tools were implemented and evaluated. The evaluation compared the two tools to manual segmentation on two cases posing different difficulties. Time-on-task and accuracy were measured during the evaluation. The evaluation revealed that the semi-automatic tools could indeed save the user time while maintaining acceptable (80%) accuracy. The significance of all results were analyzed using two-tailed t-tests.

A área médica tem se beneficiado das ferramentas construídas pela Computação e, ao mesmo tempo, tem impulsionado o desenvolvimento de novas técnicas em diversas especialidades da Computação. Dentre estas técnicas a segmentação tem como objetivo separar em uma imagem objetos de interesse, podendo chamar a atenção do profissional de saúde para áreas de relevância ao diagnóstico. Além disso, os resultados da segmentação podem ser utilizados para a reconstrução de modelos tridimensionais, que podem ter características extraídas que auxiliem o médico em tomadas de decisão. No entanto, a segmentação de imagens médicas ainda é um desafio, por ser extremamente dependente da aplicação e das estruturas de interesse presentes na imagem. Esta dissertação apresenta uma técnica de segmentação semiautomática do endocárdio do ventrículo esquerdo em conjuntos de imagens cardíacas de Ressonância Magnética Nuclear. A principal contribuição é a segmentação considerando todas as imagens provenientes de um exame, por meio da propagação dos resultados obtidos em imagens anteriormente processadas. Os resultados da segmentação são avaliados usando-se métricas objetivas como overlap, entre outras, comparando com imagens fornecidas por especialistas na área de Cardiologia / The medical field has been benefited from the tools built by Computing and has promote the development of new techniques in diverse Computer specialties. Among these techniques, the segmentation aims to divide an image into interest objects, leading the attention of the specialist to areas that are relevant in diagnosys. In addition, segmentation results can be used for the reconstruction of three-dimensional models, which may have extracted features that assist the physician in decision making. However, the segmentation of medical images is still a challenge because it is extremely dependent on the application and structures of interest present in the image. This dissertation presents a semiautomatic segmentation technique of the left ventricular endocardium in sets of cardiac images of Nuclear Magnetic Resonance. The main contribution is the segmentation considering all the images coming from an examination, through the propagation of the results obtained in previously processed images. Segmentation results are evaluated using objective metrics such as overlap, among others, compared to images provided by specialists in the Cardiology field

A área médica tem se beneficiado das ferramentas construídas pela Computação e, ao mesmo tempo, tem impulsionado o desenvolvimento de novas técnicas em diversas especialidades da Computação. Dentre estas técnicas a segmentação tem como objetivo separar em uma imagem objetos de interesse, podendo chamar a atenção do profissional de saúde para áreas de relevância ao diagnóstico. Além disso, os resultados da segmentação podem ser utilizados para a reconstrução de modelos tridimensionais, que podem ter características extraídas que auxiliem o médico em tomadas de decisão. No entanto, a segmentação de imagens médicas ainda é um desafio, por ser extremamente dependente da aplicação e das estruturas de interesse presentes na imagem. Esta dissertação apresenta uma técnica de segmentação semiautomática do endocárdio do ventrículo esquerdo em conjuntos de imagens cardíacas de Ressonância Magnética Nuclear. A principal contribuição é a segmentação considerando todas as imagens provenientes de um exame, por meio da propagação dos resultados obtidos em imagens anteriormente processadas. Os resultados da segmentação são avaliados usando-se métricas objetivas como overlap, entre outras, comparando com imagens fornecidas por especialistas na área de Cardiologia / The medical field has been benefited from the tools built by Computing and has promote the development of new techniques in diverse Computer specialties. Among these techniques, the segmentation aims to divide an image into interest objects, leading the attention of the specialist to areas that are relevant in diagnosys. In addition, segmentation results can be used for the reconstruction of three-dimensional models, which may have extracted features that assist the physician in decision making. However, the segmentation of medical images is still a challenge because it is extremely dependent on the application and structures of interest present in the image. This dissertation presents a semiautomatic segmentation technique of the left ventricular endocardium in sets of cardiac images of Nuclear Magnetic Resonance. The main contribution is the segmentation considering all the images coming from an examination, through the propagation of the results obtained in previously processed images. Segmentation results are evaluated using objective metrics such as overlap, among others, compared to images provided by specialists in the Cardiology field

This thesis deals with a graph cut approach for segmentation of the anatomical structures in volumetric medical images. The method used requires some voxels to be a priori identified as object or background seeds. The goal of this thesis is implementation of the graph cut method and construction of an interactive tool for segmentation. Selected metod's behaviour is examined on two datasets with manually-guided segmentation results. Testing is in one case focused on the influence of method parameters on segmentation results, while in the other deals with method tolerance towards various signal-to-noise and contrast-to-noise ratios on input. To assess the consistency of a given segmentation with the ground-truth the F-measure is used.

This work describes design and implementation of a tool for creating photomontages. The tool is based on methods of semi-automatic image segmentation. Work outlines problems of segmentation of image data and benefits of interaction with the user. It analyzes different approaches to interactive image segmentation, explains their principles and shows their positive and negative aspects. It also presents advantages and disadvantages of currently used photo-editing applications. Proposes application for creating photomontages which consists of two steps: Extraction of an object from picture and insertion of it into another picture. The first step uses the method of semi-automatic segmentation GrabCut based on the graph theory. The work also includes comparison between application and other applications in which it is possible to create a photomontage, and application tests done by users.

Prostate segmentation is a required step in determining the volume of a prostate, which is very important in the diagnosis and the treatment of prostate cancer. In the past, radiologists manually segment the two-dimensional cross-sectional ultrasound images. Typically, it is necessary for them to outline at least a hundred of cross-sectional images in order to get an accurate estimate of the prostate's volume. This approach is very time-consuming. To be more efficient in accomplishing this task, an automated procedure has to be developed. However, because of the quality of the ultrasound image, it is very difficult to develop a computerized method for defining boundary of an object in an ultrasound image. The goal of this thesis is to find an automated segmentation algorithm for detecting the boundary of the prostate in ultrasound images. As the first step in this endeavour, a semi-automatic segmentation method is designed. This method is only semi-automatic because it requires the user to enter four initialization points, which are the data required in defining the initial contour. The discrete dynamic contour (DDC) algorithm is then used to automatically update the contour. The DDC model is made up of a set of connected vertices. When provided with an energy field that describes the features of the ultrasound image, the model automatically adjusts the vertices of the contour to attain a maximum energy. In the proposed algorithm, Mallat's dyadic wavelet transform is used to determine the energy field. Using the dyadic wavelet transform, approximate coefficients and detailed coefficients at different scales can be generated. In particular, the two sets of detailed coefficients represent the gradient of the smoothed ultrasound image. Since the gradient modulus is high at the locations where edge features appear, it is assigned to be the energy field used to drive the DDC model. The ultimate goal of this work is to develop a fully-automatic segmentation algorithm. Since only the initialization stage requires human supervision in the proposed semi-automatic initialization algorithm, the task of developing a fully-automatic segmentation algorithm is reduced to designing a fully-automatic initialization process. Such a process is introduced in this thesis. In this work, the contours defined by the semi-automatic and the fully-automatic segmentation algorithm are compared with the boundary outlined by an expert observer. Tested using 8 sample images, the mean absolute difference between the semi-automatically defined and the manually outlined boundary is less than 2. 5 pixels, and that between the fully-automatically defined and the manually outlined boundary is less than 4 pixels. Automated segmentation tools that achieve this level of accuracy would be very useful in assisting radiologists to accomplish the task of segmenting prostate boundary much more efficiently.

Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-08-16T21:13:39Z No. of bitstreams: 1 HeberSousa.pdf: 2248069 bytes, checksum: e89eac1d4562ac1f2f53007d699f9c71 (MD5) / Made available in DSpace on 2017-08-16T21:13:39Z (GMT). No. of bitstreams: 1 HeberSousa.pdf: 2248069 bytes, checksum: e89eac1d4562ac1f2f53007d699f9c71 (MD5) Previous issue date: 2013-01-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Lung cancer is one of the most common of malignant tumors. It also has one of the highest rates of mortality among cancers. The reason for this is mainly linked to late diagnosis of the disease. For early detection of disease is very helpful to use medical images as support, the most important being, CT. With the acquisition of digital images is becoming more common to use computer systems for medical imaging. These systems assist in the clinical diagnosis, disease monitoring, and in some cases is used as a support for surgery. Because the search for new ways of human-computer interaction, natural interaction arises, which aims to provide a form of control with higher cognition. This control is usually performed using gestures. Interactions of gestures can be useful in controlling medical imaging systems and can ensure necessary sterility in operating rooms, because they are not required contacts manuals. Among the activities computer assisted important for the treatment of lung cancer, we have the segmentation of nodules. The segmentation of nodules can be performed automatically, semiautomatically or interactively. It is useful to speed up the diagnostic process, taking measurements, or observe the morphological appearance of the nodule. The objective of this study is to investigate the use of natural interaction interface for activities such as medical image visualization and segmentation of pulmonary nodules. The paper proposes the study of interaction techniques based on gestures to segment nodules in an interactive and semiautomatic. Finally, conducting experiments to evaluate the techniques proposed in the items ease of use, intuitiveness, accuracy and comfortability / O câncer de pulmão é um dos mais comuns dentre os tumores malignos. Ele também possui uma das taxas mais altas de mortalidade dentre os tipos de câncer. O motivo disso está ligado principalmente ao diagnóstico tardio da doença. Para a sua detecção precoce é muito útil a utilização de imagens médicas como apoio, sendo a mais importante, a tomografia computadorizada. Com a aquisição digital das imagens está cada vez mais comum a utilização de sistemas computacionais de visualização médica. Estes sistemas auxiliam no diagnóstico clínico, no acompanhamento de doenças, e em alguns casos é utilizado como apoio a cirurgias. Em virtude da busca por novos meios de interação humano-computador, surge a interação natural, que objetiva uma forma de controle mais próximo cognitivamente das ações realizadas, e geralmente é realizada através de gestos. Interações por gestos podem ser úteis no controle de sistemas de visualização médica e podem garantir a esterilização necessária em salas cirúrgicas, pois não são necessários contatos manuais. Dentre as atividades assistidas por computador importantes para o tratamento do câncer pulmonar, temos a segmentação de nódulos. A segmentação de nódulos pode ser realizada de forma automática, semiautomática ou interativamente. Elas são úteis para agilizar o processo de diagnóstico, realizar medições, ou observar o aspecto morfológico do nódulo. O objetivo do presente trabalho é investigar a utilização da interação natural como interface para atividades de visualização de imagens médicas e segmentação de nódulos pulmonares. Foi implementada uma série de ferramentas de segmentação, interativas e semiautomáticas, controladas a partir de gestos. Estes gestos foram desenvolvidos a partir de imagens capturadas por uma câmera especial chamada Kinect, que traduz a imagem em mapas de profundidade, podendo medir com precisão a distância de objetos na cena. Ao final do estudo, foi realizado experimentos para avaliar as técnicas propostas nos quesitos facilidade de uso, intuitividade, conforto e precisão.