Magnetic resonance (MR) images of the tongue have been used in both clinical medicine and scientific research
to reveal tongue structure and motion. In order to see different features of the tongue and its relation to the
vocal tract it is beneficial to acquire three orthogonal image stacks-e.g., axial, sagittal and coronal volumes. In
order to maintain both low noise and high visual detail, each set of images is typically acquired with in-plane
resolution that is much better than the through-plane resolution. As a result, any one data set, by itself, is
not ideal for automatic volumetric analyses such as segmentation and registration or even for visualization when
oblique slices are required. This paper presents a method of super-resolution reconstruction of the tongue that
generates an isotropic image volume using the three orthogonal image stacks. The method uses preprocessing
steps that include intensity matching and registration and a data combination approach carried out by Markov
random field optimization. The performance of the proposed method was demonstrated on five clinical datasets,
yielding superior results when compared with conventional reconstruction methods.
Harmonic phase (HARP) motion analysis is widely used in the analysis of tagged magnetic resonance images
of the heart. HARP motion tracking can yield gross errors, however, when there is a large amount of motion
between successive time frames. Methods that use spatial continuity of motion - so-called refinement methods -
have previously been reported to reduce these errors. This paper describes a new refinement method based on
shortest-path computations. The method uses a graph representation of the image and seeks an optimal tracking
order from a specified seed to each point in the image by solving a single source shortest path problem. This
minimizes the potential for path dependent solutions which are found in other refinement methods. Experiments
on cardiac motion tracking shows that the proposed method can track the whole tissue more robustly and is also
This paper introduces a probabilistic shortest path approach to extract the esophagus from CT images. In this modality, the absence of strong discriminative features in the observed image make the problem ill-posed without the introduction of additional knowledge constraining the problem. The solution presented in this paper
relies on learning and integrating contextual information. The idea is to model spatial dependency between the structure of interest and neighboring organs that may be easier to extract. Observing that the left atrium (LA) and the aorta are such candidates for the esophagus, we propose to learn the esophagus location with respect
to these two organs. This dependence is learned from a set of training images where all three structures have been segmented. Each training esophagus is registered to a reference image according to a warping that maps exactly the reference organs. From the registered esophagi, we define the probability of the esophagus centerline relative to the aorta and LA. To extract a new centerline, a probabilistic criterion is defined from a Bayesian formulation that combines the prior information with the image data. Given a new image, the aorta and LA are first segmented and registered to the reference shapes and then, the optimal esophagus centerline is obtained with a shortest path algorithm. Finally, relying on the extracted centerline, coupled ellipse fittings allow a robust detection of the esophagus outer boundary.
We propose a new fuzzy segmentation framework that incorporates the idea of super-resolution image reconstruction. The new framework is designed to segment data sets comprised of orthogonally acquired magnetic resonance (MR) images by taking into account their different system point spread functions. Formulating the reconstruction within the segmentation framework improves its robustness and stability, and makes it possible to incorporate multispectral scans that possess different contrast properties into the super-resolution reconstruction process. Our method has been tested on both simulated and real 3D MR brain data.