Técnicas de computação natural para segmentação de imagens médicas

Image segmentation is one of the image processing problems that deserves special attention from the scientific community. This work studies unsupervised methods to clustering and pattern recognition applicable to medical image segmentation. Natural Computing based methods have shown very attractive in such tasks and are studied here as a way to verify it's applicability in medical image segmentation. This work treats to implement the following methods: GKA (Genetic K-means Algorithm), GFCMA (Genetic FCM Algorithm), PSOKA (PSO and K-means based Clustering Algorithm) and PSOFCM (PSO and FCM based Clustering Algorithm). Besides, as a way to evaluate the results given by the algorithms, clustering validity indexes are used as quantitative measure. Visual and qualitative evaluations are realized also, mainly using data given by the BrainWeb brain simulator as ground truth

Desenvolvimento de antenas de microfita com aberturas nos patches condutores através do método da segmentação

Microstrip antennas are widely used in modern telecommunication systems. This is particularly due to the great variety of geometries and because they are easily built and integrated to other high frequency devices and circuits. This work presents a study of the properties of the microstrip antenna with an aperture impressed in the conducting patch. Besides, the analysis is performed for isotropic and anisotropic dielectric substrates. The Multiport Network Model MNM is used in combination with the Segmentation Method and the Greens function technique in the analysis of the considered microstrip antenna geometries. The numerical analysis is performed by using the boundary value problem solution, by considering separately the impedance matrix of the structure segments. The analysis for the complete structure is implemented by choosing properly the number and location of the neighboor element ports. The numerial analysis is performed for the following antenna geometries: resonant cavity, microstrip rectangular patch antenna, and microstrip rectangular patch antenna with aperture. The analysis is firstly developed for microstrip antennas on isotropic substrates, and then extended to the case of microstrip antennas on anisotropic substrates by using a Mapping Method. The experimental work is described and related to the development of several prototypes of rectangular microstrip patch antennas wtih and without rectangular apertures. A good agreement was observed between the simulated and measured results. Thereafter, a good agreement was also observed between the results of this work and those shown in literature for microstrip antennas on isotropic substrates. Furthermore, results are proposed for rectangular microstrip patch antennas wtih rectangular apertures in the conducting patch

Paralelização em GPU da segmentação vascular com extração de Centerlines por Height Ridges

The vascular segmentation is important in diagnosing vascular diseases like stroke and is hampered by noise in the image and very thin vessels that can pass unnoticed. One way to accomplish the segmentation is extracting the centerline of the vessel with height ridges, which uses the intensity as features for segmentation. This process can take from seconds to minutes, depending on the current technology employed. In order to accelerate the segmentation method proposed by Aylward [Aylward & Bullitt 2002] we have adapted it to run in parallel using CUDA architecture. The performance of the segmentation method running on GPU is compared to both the same method running on CPU and the original Aylward s method running also in CPU. The improvemente of the new method over the original one is twofold: the starting point for the segmentation process is not a single point in the blood vessel but a volume, thereby making it easier for the user to segment a region of interest, and; the overall gain method was 873 times faster running on GPU and 150 times more fast running on the CPU than the original CPU in Aylward

Segmentação Fuzzy de Texturas e Vídeos

The segmentation of an image aims to subdivide it into constituent regions or objects that have some relevant semantic content. This subdivision can also be applied to videos. However, in these cases, the objects appear in various frames that compose the videos. The task of segmenting an image becomes more complex when they are composed of objects that are defined by textural features, where the color information alone is not a good descriptor of the image. Fuzzy Segmentation is a region-growing segmentation algorithm that uses affinity functions in order to assign to each element in an image a grade of membership for each object (between 0 and 1). This work presents a modification of the Fuzzy Segmentation algorithm, for the purpose of improving the temporal and spatial complexity. The algorithm was adapted to segmenting color videos, treating them as 3D volume. In order to perform segmentation in videos, conventional color model or a hybrid model obtained by a method for choosing the best channels were used. The Fuzzy Segmentation algorithm was also applied to texture segmentation by using adaptive affinity functions defined for each object texture. Two types of affinity functions were used, one defined using the normal (or Gaussian) probability distribution and the other using the Skew Divergence. This latter, a Kullback-Leibler Divergence variation, is a measure of the difference between two probability distributions. Finally, the algorithm was tested in somes videos and also in texture mosaic images composed by images of the Brodatz album

Segmentação fuzzy de imagens e vídeos

Image segmentation is the process of subdiving an image into constituent regions or objects that have similar features. In video segmentation, more than subdividing the frames in object that have similar features, there is a consistency requirement among segmentations of successive frames of the video. Fuzzy segmentation is a region growing technique that assigns to each element in an image (which may have been corrupted by noise and/or shading) a grade of membership between 0 and 1 to an object. In this work we present an application that uses a fuzzy segmentation algorithm to identify and select particles in micrographs and an extension of the algorithm to perform video segmentation. Here, we treat a video shot is treated as a three-dimensional volume with different z slices being occupied by different frames of the video shot. The volume is interactively segmented based on selected seed elements, that will determine the affinity functions based on their motion and color properties. The color information can be extracted from a specific color space or from three channels of a set of color models that are selected based on the correlation of the information from all channels. The motion information is provided into the form of dense optical flows maps. Finally, segmentation of real and synthetic videos and their application in a non-photorealistic rendering (NPR) toll are presented

Análise morfométrica do colágeno dérmico a partir da segmentação por conglomerados (clusters) de cor

Análise morfométrica do colágeno dérmico pode fornecer subsídio quantitativo para a pesquisa em dermatologia. Os autores demonstram uma técnica de análise de imagem digital que permite a identificação de estruturas microscópicas, a partir da segmentação por conglomerados (clusters), de cor aplicada à estimativa da intensidade e densidade das fibras colágenas da derme.

Segmentação fuzzy de imagens coloridas com características texturais: uma aplicação a rochas sedimentares

Image segmentation is the process of labeling pixels on di erent objects, an important step in many image processing systems. This work proposes a clustering method for the segmentation of color digital images with textural features. This is done by reducing the dimensionality of histograms of color images and using the Skew Divergence to calculate the fuzzy a nity functions. This approach is appropriate for segmenting images that have colorful textural features such as geological, dermoscopic and other natural images, as images containing mountains, grass or forests. Furthermore, experimental results of colored texture clustering using images of aquifers' sedimentary porous rocks are presented and analyzed in terms of precision to verify its e ectiveness.

Segmentação anatomocirúrgica arterial dos pulmões de ovinos da raça ideal (Ovis aires - L.1758)

Foram estabelecidos segmentos anatomocirúrgicos em pulmões de ovinos da raça Ideal (dezessete segmentos no pulmão direito e doze no esquerdo), mediante dissecação de peças coradas com látex colorido e fixadas em formol. Na maioria dos casos, a artéria pulmonar direita emite, a partir de um tronco, o ramo ascendente e descendente para as partes cranial e caudal do lobo cranial respectivamente; o ramo do lobo médio; o ramo do lobo caudal e o ramo do lobo acessório. Invariavelmente, a artéria pulmonar esquerda emite o ramo do lobo cranial e o ramo do lobo caudal.

Segmentação de dados de perfilamento a laser em áreas urbanas utilizando uma abordagem Bayesiana

In this paper is presented a region-based methodology for Digital Elevation Model segmentation obtained from laser scanning data. The methodology is based on two sequential techniques, i.e., a recursive splitting technique using the quad tree structure followed by a region merging technique using the Markov Random Field model. The recursive splitting technique starts splitting the Digital Elevation Model into homogeneous regions. However, due to slight height differences in the Digital Elevation Model, region fragmentation can be relatively high. In order to minimize the fragmentation, a region merging technique based on the Markov Random Field model is applied to the previously segmented data. The resulting regions are firstly structured by using the so-called Region Adjacency Graph. Each node of the Region Adjacency Graph represents a region of the Digital Elevation Model segmented and two nodes have connectivity between them if corresponding regions share a common boundary. Next it is assumed that the random variable related to each node, follows the Markov Random Field model. This hypothesis allows the derivation of the posteriori probability distribution function whose solution is obtained by the Maximum a Posteriori estimation. Regions presenting high probability of similarity are merged. Experiments carried out with laser scanning data showed that the methodology allows to separate the objects in the Digital Elevation Model with a low amount of fragmentation.

Segmentação de palavras: prosódia e convenções ortográficas na reelaboração da escrita infantil

Pós-graduação em Estudos Linguísticos - IBILCE

Segmentação morfológica aplicada à cartografia

Pós-graduação em Ciências Cartográficas - FCT

Materialografia quantitativa de micro estruturas complexas baseada na segmentação por texturas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Segmentação de imagens e reconstrução de modelos aplicada a estruturas ósseas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Flutuação na segmentação de palavras: relações entre constituintes prosódicos e convenções ortográficas na escrita infantil

In this paper, we analyzed situations where (a) one vocabular structure showed in two different ways in the same text or (b) had erasures. These structures fluctuations were extracted from texts written by children that, when the registers were done, were second graders of elementary school. Concerning the results, we verified: (1) that more than one prosodic constituent showed in the basis of fluctuation of vocabulary structures; and (2) that at least one of the limits of orthographic words was maintained in fluctuation structures. These results point to the recuperation done by writers with information they have access due to their insertion in (1) oral practices and (2) literacy practices.

Contagem automatizada de veículos utilizando segmentação estatística

In this work was developed a program capable of performing automatic counting of vehicles on roads. The problem of counting vehicles is using expensive techniques for its realization, techniques which often involve manual counting or degradation of the pavement. The main motivation for this work was the importance that the vehicle counting represents to the Traffic Engineer, being essential to analyze the performance of the roads, allowing to measure the need for installation of traffic lights, roundabouts, access ways, among other means capable of ensuring a continuous flow and safe for vehicles. The main objective of this work was to apply a statistical segmentation technique recently developed, based on a nonparametric linear regression model, to solve the segmentation problem of the program counter. The development program was based on the creation of three major modules, one for the segmentation, another for the tracking and another for the recognition. For the development of the segmentation module, it was applied a statistical technique combined with the segmentation by background difference, in order to optimize the process. The tracking module was developed based on the use of Kalman filters and application of simple concepts of analytical geometry. To develop the recognition module, it was used Fourier descriptors and a neural network multilayer perceptron, trained by backpropagation. Besides the development of the modules, it was also developed a control logic capable of performing the interconnection among the modules, mainly based on a data structure called state. The analysis of the results was applied to the program counter and its component modules, and the individual analysis served as a means to establish the par ameter values of techniques used. The find result was positive, since the statistical segmentation technique proved to be very useful and the developed program was able to count the vehicles belonging to the three goal..