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Proceedings Paper

Polynomial transformation for MRI feature extraction
Author(s): Hamid Soltanian-Zadeh; Mahmood Kharrat; Donald J. Peck
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Paper Abstract

We present a non-linear (polynomial) transformation to minimize scattering of data points around normal tissue clusters in a normalized MRI feature space, in which normal tissues are clustered around pre-specified target positions. This transformation is motivated by non-linear relationship between MRI pixel intensities and intrinsic tissue parameters (e.g., T1, T2, PD). To determine scattering amount, we use ratio of summation of within-class distances fro clusters to summation of their between-class distances. We find the transformation by minimizing the scattering amount. Next, we generate a 3D visualization of the MRI feature space and define regions of interest (ROI's) on clusters seen for normal and abnormal tissues. We use these ROI's to estimate signature vectors (cluster centers). Finally, we use the signature vectors for segmenting and characterizing tissues. We used simulation, phantom, and brain MRI to evaluate the polynomial transformation and compare it to the linear transformation. In all studies, we were able to identify clusters for normal and abnormal tissues and segment the images. Compared to the linear method, the non-linear approach yields enhanced clustering properties and better separation of normal and abnormal tissues. ON the other hand, the linear transformation is more appropriate than the non-linear method for capturing partial volume information.

Paper Details

Date Published: 3 July 2001
PDF: 11 pages
Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); doi: 10.1117/12.430991
Show Author Affiliations
Hamid Soltanian-Zadeh, Univ. of Tehran (Iran) and Henry Ford Health System (United States)
Mahmood Kharrat, Univ. of Tehran and Iran Telecommunication Research Ctr. (Iran)
Donald J. Peck, Henry Ford Health System (United States)


Published in SPIE Proceedings Vol. 4322:
Medical Imaging 2001: Image Processing
Milan Sonka; Kenneth M. Hanson, Editor(s)

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