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Intensity inhomogeneity often occurs in real-world images, which presents a considerable challenge in image segmentation. The most widely used image segmentation algorithms are region-based and typically rely on the homogeneity of the image intensities in the regions of interest, which often fail to provide accurate segmentation results due to the intensity inhomogeneity. This paper proposes a novel...
In this paper we present a novel tag removal method for tagged cardiac MR images. This method is based on a 2D band-stop filtering technique, which selectively attenuates the tag pattern's frequency components in the spatial-frequency domain. To automatically design the band-stop filter, we developed a mean shift-based clustering method to partition the power spectrum image into several harmonic regions...
Deformable models have had great successes over the past 20 years in medical applications. We have recently developed new classes of deformable models which we term hybrid deformable models to automate the model initialization process and make improvements in segmentation and registration. In this paper we present several hybrid deformable methods we have been developing for segmentation and registration...
Most hybrid 3D segmentation methods either heuristically couple the respective algorithm or combine a true 3D with a 2D algorithm due to computational considerations. In this paper we propose a new probabilistic framework for 3D image segmentation that combines tightly linked region- and shape-based constraints. Region-based label constraints are modeled by a 3D Markov random field, and are tightly...
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