Biomedical subjects

Jorge S Marques

Publications and source records attributed to Jorge S Marques.

3 recordsLinked to original sources

PubMed2005

Adaptive snakes using the EM algorithm.

Deformable models (e.g., snakes) perform poorly in many image analysis problems. The contour model is attracted by edge points detected in the image. However, many edge points do not belong to the object contour, preventing the active contour from converging toward the object boundary. A new algorithm is proposed in this paper to overcome this difficulty. The algorithm is based on two key ideas. First, edge points are associated in strokes. Second, each stroke is classified as valid (inlier) or invalid (outlier) and a confidence degree is associated to each stroke. The expectation maximization algorithm is used to update the confidence degrees and to estimate the object contour. It is shown that this is equivalent to the use of an adaptive potential function which varies during the optimization process. Valid strokes receive high confidence degrees while confidence degrees of invalid strokes tend to zero during the optimization process. Experimental results are presented to illustrate the performance of the proposed algorithm in the presence of clutter, showing a remarkable robustness.

Algorithms↗

PubMed2003

Compensation of log-compressed images for 3-D ultrasound.

In this study, a Bayesian approach was used for 3-D reconstruction in the presence of multiplicative noise and nonlinear compression of the ultrasound (US) data. Ultrasound images are often considered as being corrupted by multiplicative noise (speckle). Several statistical models have been developed to represent the US data. However, commercial US equipment performs a nonlinear image compression that reduces the dynamic range of the US signal for visualization purposes. This operation changes the distribution of the image pixels, preventing a straightforward application of the models. In this paper, the nonlinear compression is explicitly modeled and considered in the reconstruction process, where the speckle noise present in the radio frequency (RF) US data is modeled with a Rayleigh distribution. The results obtained by considering the compression of the US data are then compared with those obtained assuming no compression. It is shown that the estimation performed using the nonlinear log-compression model leads to better results than those obtained with the Rayleigh reconstruction method. The proposed algorithm is tested with synthetic and real data and the results are discussed. The results have shown an improvement in the reconstruction results when the compression operation is included in the image formation model, leading to sharper images with enhanced anatomical details.

Algorithms↗

PubMed2002

A multiscale algorithm for three-dimensional free-hand ultrasound.

This paper presents a multiscale algorithm for the reconstruction of human anatomy from a set of ultrasound (US) images. Reconstruction is formulated in a Bayesian framework as an optimization problem with a large number of unknown variables. Human tissues are represented by the interpolation of coefficients associated to the nodes of a 3-D cubic grid. The convergence of the Bayesian method is usually slow and initialization dependent. In this paper, a multiscale approach is proposed to increase the convergence rate of the iterative process of volume estimation. A coarse estimate of the volume is first obtained using a cubic grid with a small number of nodes initialized with a constant value computed from the observed data. The volume estimate is then recursively improved by refining the grid step. Experimental results are provided to show that multiscale method achieves faster convergence rates compared with a single-scale approach. This is the key improvement toward real-time implementations. Experimental results of 3-D reconstruction of human anatomy are presented to assess the performance of the algorithm and comparisons with the single-scale method are presented.

Algorithms↗