A nonparametric, entropy-minimizing MRI tissue classification algorithm implementation using ITK
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
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.