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We propose a method for recursive segmentation of the left ventricle (LV) across a temporal sequence of magnetic resonance (MR) images. The approach involves a technique for learning the LV boundary dynamics together with a particle-based inference algorithm on a loopy graphical model capturing the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation of the...
This paper presents a new approach for segmentation of multiple brain structures. We introduce a new coupled shape prior for neighboring structures in magnetic resonance images (MRI) for multi object segmentation problem, where the information obtained from images can not provide enough contrast or exact boundary. In segmentation of low contrasted brain structures we take the advantage of using prior...
We propose a principled framework for recursively segmenting deformable objects across a sequence of frames. We demonstrate the usefulness of this method on left ventricular segmentation across a cardiac cycle. The approach involves a technique for learning the system dynamics together with methods of particle-based smoothing as well as nonparametric belief propagation on a loopy graphical model capturing...
The aging population in developed countries has shifted considerable research attention to diseases related to age. Because age is one of the highest risk factors for neurodegenerative diseases, the need for automated brain image analysis has significantly increased. Magnetic resonance imaging (MRI) is a commonly used modality to image brain. MRI provides high tissue contrast; hence, the existing...
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