A nonparametric, entropy-minimizing MRI tissue classification algorithm implementation using ITK
Tasdizen, Tolga, Awate, Suyash, Whitaker, Ross
University of Utah
Please use this identifier to cite or link to this publication: http://hdl.handle.net/1926/29
New: Prefer using the following doi: https://doi.org/10.54294/nohev6
Published in The Insight Journal - 2005 MICCAI Open-Source Workshop.
Submitted by Tolga Tasdizen on 2005-08-03T16:33:51Z.
This paper focuses on the role of open-source software in the development of a novel magnetic resonance image (MRI) tissue classification algorithm. Specifically, we describe the of use existing classes in the Insight Segmentation and Registration Toolkit (ITK) and several new classes that were implemented to perform non-parametric density estimation and entropy minimization. These new classes also provide a general framework for nonparametric density estimation and related applications.
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