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Thesis

Non-parametric probability density function estimation for medical images

Abstract:

The estimation of probability density functions (PDF) of intensity values plays an important role in medical image analysis. Non-parametric PDF estimation methods have the advantage of generality in their application. The two most popular estimators in image analysis methods to perform the non-parametric PDF estimation task are the histogram and the kernel density estimator. But these popular estimators crucially need to be ‘tuned’ by setting a number of parameters and may be either computati...

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Research group:
Wolfson Medical Vision Laboratory
Oxford college:
Brasenose College
Role:
Author

Contributors

Role:
Supervisor
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Funding agency for:
Joshi, NB
Publication date:
2008
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford
Language:
English
Keywords:
Subjects:
UUID:
uuid:ebc6af07-770b-4fee-9dc9-5ebbe452a0c1
Local pid:
ora:2495
Deposit date:
2009-01-09

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