Análise fractal da vasculatura retínica: métodos de segmentação e de cálculo dimensional

Embora tenha sido proposto que a vasculatura retínica apresenta estrutura fractal, nenhuma padronização do método de segmentação ou do método de cálculo das dimensões fractais foi realizada. Este estudo objetivou determinar se a estimação das dimensões fractais da vasculatura retínica é dependente dos métodos de segmentação vascular e dos métodos de cálculo de dimensão. Métodos: Dez imagens retinográficas foram segmentadas para extrair suas árvores vasculares por quatro métodos computacionais (“multithreshold”, “scale-space”, “pixel classification” e “ridge based detection”). Suas dimensões fractais de “informação”, de “massa-raio” e “por contagem de caixas” foram então calculadas e comparadas com as dimensões das mesmas árvores vasculares, quando obtidas pela segmentação manual (padrão áureo). Resultados: As médias das dimensões fractais variaram através dos grupos de diferentes métodos de segmentação, de 1,39 a 1,47 para a dimensão por contagem de caixas, de 1,47 a 1,52 para a dimensão de informação e de 1,48 a 1,57 para a dimensão de massa-raio. A utilização de diferentes métodos computacionais de segmentação vascular, bem como de diferentes métodos de cálculo de dimensão, introduziu diferença estatisticamente significativa nos valores das dimensões fractais das árvores vasculares. Conclusão: A estimação das dimensões fractais da vasculatura retínica foi dependente tanto dos métodos de segmentação vascular, quanto dos métodos de cálculo de dimensão utilizados

Correspondência estéreo usando imagens em multiresolução com profundidade variável

We propose a multi-resolution, coarse-to-fine approach for stereo matching, where the first matching happens at a different depth for each pixel. The proposed technique has the potential of attenuating several problems faced by the constant depth algorithm, making it possible to reduce the number of errors or the number of comparations needed to get equivalent results. Several experiments were performed to demonstrate the method efficiency, including comparison with the traditional plain correlation technique, where the multi-resolution matching with variable depth, proposed here, generated better results with a smaller processing time

Compressão auto-adaptativa de imagens coloridas

Image compress consists in represent by small amount of data, without loss a visual quality. Data compression is important when large images are used, for example satellite image. Full color digital images typically use 24 bits to specify the color of each pixel of the Images with 8 bits for each of the primary components, red, green and blue (RGB). Compress an image with three or more bands (multispectral) is fundamental to reduce the transmission time, process time and record time. Because many applications need images, that compression image data is important: medical image, satellite image, sensor etc. In this work a new compression color images method is proposed. This method is based in measure of information of each band. This technique is called by Self-Adaptive Compression (S.A.C.) and each band of image is compressed with a different threshold, for preserve information with better result. SAC do a large compression in large redundancy bands, that is, lower information and soft compression to bands with bigger amount of information. Two image transforms are used in this technique: Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA). Primary step is convert data to new bands without relationship, with PCA. Later Apply DCT in each band. Data Loss is doing when a threshold discarding any coefficients. This threshold is calculated with two elements: PCA result and a parameter user. Parameters user define a compression tax. The system produce three different thresholds, one to each band of image, that is proportional of amount information. For image reconstruction is realized DCT and PCA inverse. SAC was compared with JPEG (Joint Photographic Experts Group) standard and YIQ compression and better results are obtain, in MSE (Mean Square Root). Tests shown that SAC has better quality in hard compressions. With two advantages: (a) like is adaptive is sensible to image type, that is, presents good results to divers images kinds (synthetic, landscapes, people etc., and, (b) it need only one parameters user, that is, just letter human intervention is required

Padrão de distribuição e caracterização morfológica de fibras serotonérgicas nos núcleos da linha Média/ intralaminares do tálamo do mocó (Kerodon rupestris)

The midline/intralaminar nuclei form a remarkable group of nuclei of the medial and dorsal thalamus. The midline nuclei, in rats, comprises the paratenial nuclei (PT), paraventricular (PV), intermediodorsal (IMD), reuniens (Re) and rhomboid (Rh). The intralaminar nuclei comprises the central medial (CM), paracentral (PC), central lateral (CL) and parafascicular (PF). Such nuclei have dense serotonergic innervation originating from the brainstem, especially from the so-called ascending activation system. These nuclei, in turn, send projections to various cortical and subcortical areas, specifically to limbic areas, which suggests the important role of this neurotransmitter in the limbic circuitry. The aim of this study was to characterize the distribution pattern and morphology of serotonin fibers in the nuclei of the midline and intralaminar thalamic of rocky cavy (Kerodon rupestris), a tipical rodent from brazilizan northeast. To reach this aim we used four rock cavies adults. Following the transcardially perfusion with paraformaldehyde and brain microtomy steps was performed immunohistochemistry for serotonin (5-HT), Nissl technique and subsequent achievement and image analysis to characterize the cytoarchitecture of these nuclei and the serotonergic fibers visualized. An analysis was made of Relative Optical Density (ROD) to semi-quantify the concentration of serotonin fibers in the areas of interest. Thus, we observed a cytoarchitectonic arrangement of these nuclei similar to that found in rats. In case of fibers distribution, those immunoreactive to 5-HT were presented in a higher concentration according as ROD in the midline nuclei relative to intralaminar; Re being the core which has a higher pixel value followed by the PV , Rh, IMD and PT. In intralaminar CL showed higher pixels, followed by nuclei CM, PC and PF. The serotonergic fibers were classified as number of varicosities and axon diameter, therefore find three types of fibers distributed through this nuclear complex: fibers rugous, granular and semi-granular. In PV fibers predominated rugous; in PT fibers predominated granular; IMD, CL and PF fibers were represented by semi-granular and Re, Rh, PC and CM fibers showed granular and semi-granular. Morphological characterization of serotonergic fibers and differences in density between the nuclei may suggest different patterns of synaptic organization of this neurotransmitter beyond confirming his large repertoire functional

Implementação Hardware/Software da estimação de movimento segundo o padrão H.264

Motion estimation is the main responsible for data reduction in digital video encoding. It is also the most computational damanding step. H.264 is the newest standard for video compression and was planned to double the compression ratio achievied by previous standards. It was developed by the ITU-T Video Coding Experts Group (VCEG) together with the ISO/IEC Moving Picture Experts Group (MPEG) as the product of a partnership effort known as the Joint Video Team (JVT). H.264 presents novelties that improve the motion estimation efficiency, such as the adoption of variable block-size, quarter pixel precision and multiple reference frames. This work defines an architecture for motion estimation in hardware/software, using a full search algorithm, variable block-size and mode decision. This work consider the use of reconfigurable devices, soft-processors and development tools for embedded systems such as Quartus II, SOPC Builder, Nios II and ModelSim

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