Modelos Deformáveis em Imagem Médica

Modelos deformáveis são actualmente bastante utilizados em imagem médica pois, através da utilização de princípios físicos, simulam de forma bastante satisfatória o comportamento dos objectos reais.Basicamente os modelos deformáveis são inicializados junto dos objectos a considerar, por processos automáticos ou semi-automáticos, e a aproximação para a posição final desejada é conseguida através de um processo de minimização de energia. Esta minimização de energia é verificada quando o modelo atinge o equilíbrio, entre as suas forças internas e as forças externas originadas pelos dados e por eventuais forças impostas pelo utilizador.Neste relatório são apresentados os fundamentos dos modelos deformáveis e indicados alguns exemplos de aplicação em imagem médica, nomeadamente na segmentação, no emparelhamento, no alinhamento e na reconstrução de dados 2D e 3D.Palavras-chave: Contornos activos, imagem médica, modelos deformáveis.Deformable models are currently very used in medical image since, through the use of physical principles, they simulate quite satisfactory the real objects behavior.Basically the deformable models are placed in the image near to the objects to be considered, by automatic or semi-automatic processes, and the approach to the desired final position is obtained through an energy minimization process. This energy minimization is verified when the model reaches the equilibrium, between its internal forces and the external forces originated by the data and eventual forces imposed by the user.In this report are presented the deformable models fundaments and indicated some application examples in medical imaging field, namely in segmentation, matching, alignment and in the reconstruction of 2D and 3D data.Keywords: Active contours, deformable models, medical image

An Eye-Contour Extraction Algorithm from Face Image usingDeformable Template Matching

A variety of studies on face components such as eyes, lips, noses, and teeth have been proceeding in medicine, psychology, biometrics authentication, and other areas. In this paper, we present an algorithm of extracting eye contours from a face image using the deformable template matching method. Our template for an eye contour is composed of three quadratic functions for the perimeter and one circle for the pupil. In our algorithm, a digital color face image is rst converted to a binary image of representing eyes, after the region around eyes is identied on the face image by using hues and values of the color image. Then, parameters in the template are optimized by a local search method with a tabu period and a hill-climbing, so as to t the template to the eye contour in the binary image. The accuracy of our algorithm is evaluated through sample face images of students. In addition, the application of our proposal to eye shape indices is investigated in a face image database "HOIP", where recognizable dierence exists in index distributions between males and females

3D facial data fitting using the biharmonic equation

This paper discusses how a boundary-based surface fitting approach can be utilised to smoothly reconstruct a given human face where the scan data corresponding to the face is provided. In particular, the paper discusses how a solution to the Biharmonic equation can be used to set up the corresponding boundary value problem. We show how a compact explicit solution method can be utilised for efficiently solving the chosen Biharmonic equation. Thus, given the raw scan data of a 3D face, we extract a series of profile curves from the data which can then be utilised as boundary conditions to solve the Biharmonic equation. The resulting solution provides us a continuous single surface patch describing the original face

Lip Image Feature Extraction Utilizing Snake’s Control Points for Lip Reading Applications

Snake is an active contour model that catches and locks image edges, then localizes them accurately. The simplest Snake consists of a set of control points that are connected by straight lines to form a closed loop. This paper discusses the application of Snake to find the visual feature of lip shapes. In most previous papers, visual feature of lip shapes is represented by Snake’s contour. In this paper, the feature of lip shapes is represented by six control points on lip Snake’s contours. By simply utilizing six control points representing one lip Snake’s contour, it is expected to reduce the burden on pattern recognition stage. To demonstrate the performance of this method, some analysis has been conducted on the effect of lip conditions and illumination. The results shows that the overall lip feature extraction using the proposed method is better for lips that have more contrast to the surrounding skin, optimum room illumination that gives the best result is in the range of 330-340 lux