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A hierarchical spectral clustering and nonlinear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS)
Accepted manuscript   Open access

A hierarchical spectral clustering and nonlinear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS)

Pallavi Tiwari, Mark Rosen and Anant Madabhushi
Medical Physics, Vol.36(9), pp.3927-3939
2009
DOI:
https://doi.org/10.7282/T389144H

Abstract

magnetic resonance spectroscopy computer-aided diagnosis Diagnostic imaging Prostate--Cancer nonlinear dimensionality reduction hierarchical clustering unsupervised classification Magnetic Resonance Imaging
In this article the authors present a novel CAD scheme that integrates nonlinear dimensionality reduction (NLDR) with an unsupervised hierarchical clustering algorithm to automatically identify suspicious regions on the prostate using MRS and hence avoids the need to explicitly identify metabolite peaks.
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Accepted Manuscript (AM) Open Access
url
http://dx.doi.org/10.1118/1.3180955View
Version of Record (VoR)Medical Physics

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