Sheshadri R. Thiruvenkadam

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In this paper, we report an active contour algorithm that is capable of using prior shapes. The energy functional of the contour is modified so that the energy depends on the image gradient as well as the prior shape. The model provides the segmentation and the transformation that maps the segmented contour to the prior shape. The active contour is able to(More)
A simultaneous segmentation and registration model is proposed for functional MR (fMR) image alignment that is significant for minimizing motion artifacts on fMRI data analysis. Due to weighted signal loss and decreased resolution, in fMR images, the images can’t be aligned reliably by only using image information. Our approach uses the shape of a contour(More)
Surface registration, which transforms different sets of surface data into one common reference space, is an important process which allows us to compare or integrate the surface data effectively. If a nonrigid transformation is required, surface registration is commonly done by parameterizing the surfaces onto a simple parameter domain, such as the unit(More)
In this work, we present a novel region-based active contour technique to extract the breast boundary in mammograms. Skinline extraction in mammograms is non-trivial due to the presence of noise and scanning artifacts. Also, weak contrast near the skin-line boundary, especially in the case of low-density breasts, poses a lot of difficulty in its extraction.(More)
In this work, we find meaningful parameterizations of cortical surfaces utilizing prior anatomical information in the form of anatomical landmarks (sulci curves) on the surfaces. Specifically we generate close to conformal parametrizations that also give a shape-based correspondence between the landmark curves. We propose a variational energy that measures(More)
In this work, we address the problem of segmenting multiple objects, under possible occlusions, in a level set framework. A variational energy that incorporates a piecewise constant representation of the image in terms of the object regions and the object spatial order is proposed. To resolve occluded boundaries, prior knowledge of shape of objects is also(More)
We propose a novel variational approach to automatically soft-segment medical images into a fixed number of classes. Our method combines fuzzy classification and active contours in a single variational framework. This approach allows the use of tools from both de-formable geometry and clustering in a well-defined setting and provides a useful, unsupervised(More)