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11 May 1994 Image resolution: the impact on finite mixture density models in medical applications
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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.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Howard Donald Gage, Fredrick H Fahey, William H. Hinson, and Peter Santago II "Image resolution: the impact on finite mixture density models in medical applications", Proc. SPIE 2167, Medical Imaging 1994: Image Processing, (11 May 1994); https://doi.org/10.1117/12.175106
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