Bhagalia
Roshni Bhagalia, Ann Arbor, MI US
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20090245611 | Method and Apparatus for Removing Tissue Autofluorescence - Techniques for removing image autoflourescence from fluorescently stained biological images are provided herein. The techniques utilize non-negative matrix factorization that may constrain mixing coefficients to be non-negative. The probability of convergence to local minima is reduced by using smoothness constraints. The non-negative matrix factorization algorithm provides the advantage of removing both dark current and autofluorescence. | 10-01-2009 |
Roshni Bhagalia, Niskayuna, NY US
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20150086101 | METHOD FOR ORGAN LOCALIZATION - In some embodiments, a method for localizing organs in anatomical imaging may include: performing an anterior-posterior view scan and a lateral view scan to create an anterior-posterior view scan image and a lateral view scan image; creating a joint anatomical model based on the anterior-posterior scan image and the lateral view scan image; and refining the joint anatomical model. | 03-26-2015 |
Roshni Rustom Bhagalia, Niskayuna, NY US
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20130272596 | METHODS FOR GENERATING A RECONSTRUCTED TREE STRUCTURE FROM MEDICAL IMAGE DATA - A computer-implemented method of post-processing medical image data is provided. The method includes receiving tracked image data representative of multiple blood vessels, generating a binary tree structure for the multiple blood vessels based on a parent-child relationship between branches of the multiple blood vessels, generating a likelihood model for determining a validity of the branches of the multiple blood vessels, and generating a likelihood score for each branch based on the respective branch's compatibility with the likelihood model. The method also includes generating a reconstructed tree for the multiple blood vessels. Compatible branches are included in the reconstructed tree, while invalid branches are not included in the reconstructed tree. | 10-17-2013 |
20140294276 | AUTOMATIC ESTIMATION OF ANATOMICAL EXTENTS - A hierarchical multi-object active appearance model (AAM) framework is disclosed for processing image data, such as localizer or scout image data. In accordance with this approach, a hierarchical arrangement of models (e.g., a model pyramid) maybe employed where a global or parent model that encodes relationships across multiple co-located structures is used to obtain an initial, coarse fit. Subsequent processing by child sub-models add more detail and flexibility to the overall fit. | 10-02-2014 |