Biomedical subjects

J L Marroquin

Publications and source records attributed to J L Marroquin.

3 recordsLinked to original sources

PubMed2005

Adaptive monogenic filtering and normalization of ESPI fringe patterns.

A technique is presented for filtering and normalizing noisy fringe patterns, which may include closed fringes, so that single-frame demodulation schemes may be successfully applied. It is based on the construction of an adaptive filter as a linear combination of the responses of a set of isotropic bandpass filters. The space-varying coefficients are proportional to the envelope of the response of each filter, which in turn is computed by using the corresponding monogenic image [Felsberg and Sommer, IEEE Trans. Signal Process. 49, 3136 (2001)]. Some examples of demodulation of real Electronic Speckle Pattern Interferometry (ESPI) images patterns are presented.

Algorithms↗

PubMed2002

Local frequency representations for robust multimodal image registration.

Automatic registration of multimodal images involves algorithmically estimating the coordinate transformation required to align the data sets. Most existing methods in the literature are unable to cope with registration of image pairs with large nonoverlapping field of view (FOV). We propose a robust algorithm, based on matching dominant local frequency image representations, which can cope with image pairs with large nonoverlapping FOV. The local frequency representation naturally allows for processing the data at different scales/resolutions, a very desirable property from a computational efficiency view point. Our algorithm involves minimizing-over all rigid/affine transformations--the integral of the squared error (ISE or L2 E) between a Gaussian model of the residual and its true density function. The residual here refers to the difference between the local frequency representations of the transformed (by an unknown transformation) source and target data. We present implementation results for image data sets, which are misaligned magnetic resonance (MR) brain scans obtained using different image acquisition protocols as well as misaligned MR-computed tomography scans. We experimently show that our L2E-based scheme yields better accuracy over the normalized mutual information.

Brain↗

PubMed2002

An accurate and efficient bayesian method for automatic segmentation of brain MRI.

Automatic three-dimensional (3-D) segmentation of the brain from magnetic resonance (MR) scans is a challenging problem that has received an enormous amount of attention lately. Of the techniques reported in the literature, very few are fully automatic. In this paper, we present an efficient and accurate, fully automatic 3-D segmentation procedure for brain MR scans. It has several salient features; namely, the following. 1) Instead of a single multiplicative bias field that affects all tissue intensities, separate parametric smooth models are used for the intensity of each class. 2) A brain atlas is used in conjunction with a robust registration procedure to find a nonrigid transformation that maps the standard brain to the specimen to be segmented. This transformation is then used to: segment the brain from nonbrain tissue; compute prior probabilities for each class at each voxel location and find an appropriate automatic initialization. 3) Finally, a novel algorithm is presented which is a variant of the expectation-maximization procedure, that incorporates a fast and accurate way to find optimal segmentations, given the intensity models along with the spatial coherence assumption. Experimental results with both synthetic and real data are included, as well as comparisons of the performance of our algorithm with that of other published methods.

Algorithms↗