[Truncated abstract] Transrectal ultrasound (TRUS) has become the urologist’s primary tool for diagnosing and staging prostate cancer due to its real-time and non-invasive nature, low cost, and minimal discomfort. However, the interpretation of a prostate ultrasound image depends critically on the experience and expertise of a urologist and is still difficult and subjective. To overcome the subjective interpretation and facilitate objective diagnosis, computer aided analysis of ultrasound images of the prostate would be very helpful. Computer aided analysis of images may improve diagnostic accuracy by providing a more reproducible interpretation of the images. This thesis is an attempt to address several key elements of computer aided analysis of ultrasound images of the prostate. Specifically, it addresses the following tasks: 1. modelling B-mode ultrasound image formation and statistical properties; 2. reducing ultrasound speckle; and 3. extracting prostate contour. Speckle refers to the granular appearance that compromises the image quality and resolution in optics, synthetic aperture radar (SAR), and ultrasound. Due to the existence of speckle the appearance of a B-mode ultrasound image does not necessarily relate to the internal structure of the object being scanned. A computer simulation of B-mode ultrasound imaging is presented, which not only provides an insight into the nature of speckle, but also a viable test-bed for any ultrasound speckle reduction methods. Motivated by analysis of the statistical properties of the simulated images, the generalised Fisher-Tippett distribution is empirically proposed to analyse statistical properties of ultrasound images of the prostate. A speckle reduction scheme is then presented, which is based on Mallat and Zhong’s dyadic wavelet transform (MZDWT) and modelling statistical properties of the wavelet coefficients and exploiting their inter-scale correlation. Specifically, the squared modulus of the component wavelet coefficients are modelled as a two-state Gamma mixture. Interscale correlation is exploited by taking the harmonic mean of the posterior probability functions, which are derived from the Gamma mixture. This noise reduction scheme is applied to both simulated and real ultrasound images, and its performance is quite satisfactory in that the important features of the original noise corrupted image are preserved while most of the speckle noise is removed successfully. It is also evaluated both qualitatively and quantitatively by comparing it with median, Wiener, and Lee filters, and the results revealed that it surpasses all these filters. A novel contour extraction scheme (CES), which fuses MZDWT and snakes, is proposed on the basis of multiresolution analysis (MRA). Extraction of the prostate contour is placed in a multi-scale framework provided by MZDWT. Specifically, the external potential functions of the snake are designated as the modulus of the wavelet coefficients at different scales, and thus are “switchable”. Such a multi-scale snake, which deforms and migrates from coarse to fine scales, eventually extracts the contour of the prostate

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Many applications of digital image processing uses object detection techniques. Detecting an object is usually related to locate the area around it, while shape detection is related to nd, precisely, the set of points that constitutes its shape. When the problem involves detecting shapes that have predictable changes, deformable models show to be an e ective solution. The approach developed in this work refers to the vehicle shape detection in frontal position by methods which are divided into two levels, the rst level is composed by a cascade of support vector machines and the second one is a deformable model. The use of deformable models favors the detection of vehicle shape same when its image is occluded by objects such as trees / Muitas aplicações de processamento de imagens digitais utilizam técnicas de detecção de objetos. Detectar um objeto normalmente está relacionado a localizar a área em torno do mesmo, já a deteção da forma está relacionada a localizar precisamente em uma imagem um conjunto de pontos que constituem sua forma. Quando o problema envolve a detecção de formas que apresentam variações previsíveis, os modelos deformáveis se apresentam como uma alternativa eficaz. A abordagem desenvolvida neste trabalho se refere à detecção da forma de veículos em posição frontal através de métodos que se dividem em dois níveis, o primeiro nível é composto por uma cascata de máquinas de vetores de suporte e oo segundo é um modelo deformável. O uso de modelos deformáveis favorece a deteção de formas de veículos mesmo quando sua imagem está ocluída por objetos, tais como árvores.