Camille Vidal
Johns Hopkins University
6 Papers
158 Citations
Camille Vidal is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Image registration & Image segmentation. The author has an hindex of 3, co-authored 6 publications. Previous affiliations of Camille Vidal include Johns Hopkins University School of Medicine.
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Papers
Noninvasive Pulmonary [18F]-2-Fluoro-Deoxy-d-Glucose Positron Emission Tomography Correlates with Bactericidal Activity of Tuberculosis Drug Treatment
Stephanie L. Davis,Eric L. Nuermberger,Peter K. Um,Camille Vidal,Bruno Jedynak,Bruno Jedynak,Martin G. Pomper,William R. Bishai,Sanjay K. Jain +8 more
TL;DR: In this paper, the authors used non-invasive imaging to monitor response to TB treatment, using serial pulmonary 2-fluoro-deoxy-d-glucose positron emission tomography (PET) to monitor the response to treatment.
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Template registration with missing parts: Application to the segmentation of M. tuberculosis infected lungs
Camille Vidal,Joshua Hewitt,Stephanie L. Davis,Laurent Younes,Sanjay K. Jain,Bruno Jedynak +5 more
- 28 Jun 2009
TL;DR: A template registration technique is proposed that can be used to recover the complete segmentation of a diseased organ from a partial segmentation and is used to segment Mycobacterium tuberculosis infected lungs in CT images of experimentally infected mice.
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Learning to Match: Deriving Optimal Template-Matching Algorithms from Probabilistic Image Models
Camille Vidal,Bruno Jedynak +1 more
TL;DR: A family of statistical models for grayscale images, which allow us to derive optimal template-matching algorithms that can handle photometric variations and are exemplified on the automatic detection of anatomical landmarks in brain Magnetic Resonance Images.
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Incorporating user input in template-based segmentation
Camille Vidal,Dale Beggs,Laurent Younes,Sanjay K. Jain,Bruno Jedynak +4 more
- 01 Mar 2011
TL;DR: A simple and elegant method to incorporate user input in a template-based segmentation method for diseased organs, which minimizes the sum of square differences between the binary template and the user labels, while preventing the template from shrinking, and penalizing for the inclusion of background elements into the final segmentation.
A Statistical Approach for Detecting Tubular Structures in Myocardial Infarct Scars
Camille Vidal,Hiroshi Ashikaga,Elliot R. McVeigh +2 more
- 20 May 2009
TL;DR: An automatic method is proposed for the detection of tunnels of normal tissue through scars in high resolution MR images in order to characterize the role of such structures and facilitate arrhythmia episodes.
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