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Alok Gupta
Researcher at Siemens
Publications - 55
Citations - 2328
Alok Gupta is an academic researcher from Siemens. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 23, co-authored 55 publications receiving 2320 citations. Previous affiliations of Alok Gupta include Princeton University.
Papers
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Journal ArticleDOI
Dynamic programming for detecting, tracking, and matching deformable contours
TL;DR: The information provided by the user's selected points is explored and an optimal method to detect contours which allows a segmentation of the image is applied, based on dynamic programming (DP), and applies to a wide variety of shapes.
Proceedings ArticleDOI
Database-guided segmentation of anatomical structures with complex appearance
TL;DR: This paper introduces database-guided segmentation as a new data-driven paradigm that directly exploits expert annotation of interest structures in large medical databases and proposes a feature selection mechanism and the corresponding metric.
Patent
Systems and methods for automated diagnosis and decision support for heart related diseases and conditions
TL;DR: In this article, computer-aided diagnosis (CAD) systems and applications for cardiac imaging are provided, which implement methods to automatically extract and analyze features from a collection of patient information (including image data and/or non-image data) of a subject patient, to provide decision support for various aspects of physician workflow including, for example, automated assessment of regional myocardial function through wall motion analysis, automated diagnosis of heart diseases and conditions such as cardiomyopathy, coronary artery disease and other heartrelated medical conditions, and other automated decision support functions.
Journal ArticleDOI
An information fusion framework for robust shape tracking
TL;DR: A unified framework for robust shape tracking, optimally fusing heteroscedastic uncertainties or noise from measurement, system dynamics, and a subspace model is proposed and significantly outperforms the existing shape-space-constrained tracking algorithm.
Patent
System and Method for Detecting and Matching Anatomical Structures Using Appearance and Shape
TL;DR: In this paper, a detection framework that matches anatomical structures using appearance and shape is disclosed, where a training set of images are used in which object shapes or structures are annotated in the images.