Segmentação de imagens torácicas de Raio-X

A segmentação é uma das etapas que constituem o processamento de uma imagem. Consiste na divisão da imagem em componentes independentes. Essa informação é usada em etapas posteriores, de forma a obter-se conhecimento. Trata-se de um conceito utilizado em imagens médicas no qual irá incidir este estudo. Esta aplicação em imagens médicas torna-se um desa o devido a vários factores, como a falta de uniformidade das imagens ou à presença de elevado ruído, só para citar alguns dos obstáculos existentes. Devido a esta falta de uniformidade que é caractaterística das imagens médicas, torna-se difícil a construção de um método standard que se possa ajustar a uma grande diversidade de imagens. Este estudo pretende analisar três métodos de segmentação, aplicando-os a imagens torá- cicas de raio-x. Os três métodos são: o método de contornos activos segundo Chan e Vese, o método de contornos activos híbrido segundo Shawn Lankton e o método igualmente de contornos activos baseado em regiões segundo o mesmo autor. Esta análise é efectuada através da aplicação de 10 máscaras que servem como contornos iniciais em cada um dos três métodos. Observa-se o comportamento de cada um dos métodos no conjunto de dados analisado, com a aplicação de pré-processamento e sem aplicação de pré-processamento nas imagens, de modo a ser possível concluir qual ou quais os que apresentam melhor e pior comportamento. Uma segunda nalidade do estudo é a proposta de um modelo por parte do autor. Modelo este que se pretende que obtenha melhores resultados em termos de um menor erro de segmentação da região de interesse, em relação aos três metodos base. Este modelo proposto mostra obter melhores resultados após a conjugação de observações de cada método em relação aos resultados de cada método de uma forma isolada.Segmentation is one of the steps that constitute the processing of an image. It is the division of the image into independent components. This information is then used in later stages where it will be in order to obtain knowledge. This information is the used in later stages where it will be in order to obtain knowledge. It is a concept used in medical imaging in which this study will focus. This application in medical imaging becomes a challenge due to several factors such as the lack of uniformity of the images or to the presence of high noise, just to name some of the obstacles. Due to this lack of uniformity that is characteristic of medical images, it becomes dif cult to construct a standard method one can t a wide range of images. This study aims to analyze three segmentation methods by applying them to images of ches-x ray. The three methods are: the active contour method of chan and Vese, the hybrid active contour method by Shawn Lankton and the method based on active contours regions by the same author. This analysis is performed by applying ten masks that serve as starting contours in each of the three methods. Observe the behaviour of each method in data set analyzed by applying preprpcessing and without preprocessing application in the images, so that it is possible to conclude which of best to worst behaviour. A second purpose of the study is to propose a model by the author. In this model that is intended to obtain better results in terms of lower error segmentation of the region of interest in relation of the three base methods. This proposed model shows better results after combining observations of each method in relation to the results of each method in isolation

Investigating Polynomial Fitting Schemes for Image Compression

Image compression is a means to perform transmission or storage of visual data in the most economical way. Though many algorithms have been reported, research is still needed to cope with the continuous demand for more efficient transmission or storage. This research work explores and implements polynomial fitting techniques as means to perform block-based lossy image compression. In an attempt to investigate nonpolynomial models, a region-based scheme is implemented to fit the whole image using bell-shaped functions. The idea is simply to view an image as a 3D geographical map consisting of hills and valleys. However, the scheme suffers from high computational demands and inferiority to many available image compression schemes. Hence, only polynomial models get further considerations. A first order polynomial (plane) model is designed to work in a multiplication- and division-free (MDF) environment. The intensity values of each image block are fitted to a plane and the parameters are then quantized and coded. Blocking artefacts, a common drawback of block-based image compression techniques, are reduced using an MDF line-fitting scheme at blocks’ boundaries. It is shown that a compression ratio of 62:1 at 28.8dB is attainable for the standard image PEPPER, outperforming JPEG, both objectively and subjectively for this part of the rate-distortion characteristics. Inter-block prediction can substantially improve the compression performance of the plane model to reach a compression ratio of 112:1 at 27.9dB. This improvement, however, slightly increases computational complexity and reduces pipelining capability. Although JPEG2000 is not a block-based scheme, it is encouraging that the proposed prediction scheme performs better in comparison to JPEG 2000, computationally and qualitatively. However, more experiments are needed to have a more concrete comparison. To reduce blocking artefacts, a new postprocessing scheme, based on Weber’s law, is employed. It is reported that images postprocessed using this scheme are subjectively more pleasing with a marginal increase in PSNR (<0.3 dB). The Weber’s law is modified to perform edge detection and quality assessment tasks. These results motivate the exploration of higher order polynomials, using three parameters to maintain comparable compression performance. To investigate the impact of higher order polynomials, through an approximate asymptotic behaviour, a novel linear mapping scheme is designed. Though computationally demanding, the performances of higher order polynomial approximation schemes are comparable to that of the plane model. This clearly demonstrates the powerful approximation capability of the plane model. As such, the proposed linear mapping scheme constitutes a new approach in image modeling, and hence worth future consideration

Quality Measurement and Use of Pre-processing in Image Compression

Traditional quality measures for image coding, such as the peak signal to noise ratio, assume that the preservation of the original image is the desired goal. However, pre-processing images prior to encoding, designed to remove noise or unimportant detail, can improve the overall performance of an image coder. Objective image quality metrics obtained from the difference between the original and coded images cannot properly assess this improved performance. This paper proposes a new methodology for quality metrics that differentially weighs the changes in the image due to pre-processing and encoding. These new quality measures establish the value of pre-processing for image coding and quantitatively determine the performance improvement that can be thus achieved by JPEG and wavelet coders