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Proceedings Paper

Image resolution: the impact on finite mixture density models in medical applications
Author(s): Howard Donald Gage; Fredrick H Fahey; William H. Hinson; Peter Santago
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Paper Abstract

Finite mixture density (FMD) based approaches to medical image classification or quantification problems have received considerable interest lately. In this paper, we will show through use of computer simulations that as the resolution of the underlying imaging modality decreases (its full width at half maximum (FWHM) increases) the successful application of an FMD approach will become increasingly difficult. A 19 slice computer phantom of the human brain was used. This phantom, generated from MR images of a human brain, is composed of gray matter, white matter, and cerebrospinal fluid regions. Image sets were generated using Gaussian kernels of various sizes and FWHM's. The distributions of single and multiple components pixels were then generated from these image sets. A planar acquisition of a single slice brain phantom is also presented for comparison. It is shown that, with decreasing image resolution, a major weakness of the FMD approach is its inability to incorporate spacial information. Decreasing resolution with respect to object size results in an increasing number of partial volume pixels with resulting effects on its FMD components.

Paper Details

Date Published: 11 May 1994
PDF: 8 pages
Proc. SPIE 2167, Medical Imaging 1994: Image Processing, (11 May 1994); doi: 10.1117/12.175106
Show Author Affiliations
Howard Donald Gage, Bowman Gray School of Medicine/Wake Forest Univ. (United States)
Fredrick H Fahey, Bowman Gray School of Medicine/Wake Forest Univ. (United States)
William H. Hinson, Bowman Gray School of Medicine/Wake Forest Univ. (United States)
Peter Santago, Bowman Gray School of Medicine/Wake Forest Univ. (United States)


Published in SPIE Proceedings Vol. 2167:
Medical Imaging 1994: Image Processing
Murray H. Loew, Editor(s)

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