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Proceedings Paper

Inhomogeneity correction for magnetic resonance images with fuzzy C-mean algorithm
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Paper Abstract

Segmentation of magnetic resonance (MR) images plays an important role in quantitative analysis of brain tissue morphology and pathology. However, the inherent effect of image-intensity inhomogeneity renders a challenging problem and must be considered in any segmentation method. For example, the adaptive fuzzy c-mean (AFCM) image segmentation algorithm proposed by Pham and Prince can provide very good results in the presence of the inhomogeneity effect under the condition of low noise levels. Their results deteriorate quickly as the noise level goes up. In this paper, we present a new fuzzy segmentation algorithm to improve the noise performance of the AFCM algorithm. It achieves accurate segmentation in the presence of inhomogeneity effect and high noise levels by incorporating the spatial neighborhood information into the objective function. This new algorithm was tested by both simulated experimental and real clinical MR images. The results demonstrated the improved performance of this new algorithm over the AFCM in the clinical environment where the inhomogeneity and noise levels are commonly encountered.

Paper Details

Date Published: 15 May 2003
PDF: 11 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.481375
Show Author Affiliations
Xiang Li, SUNY/Stony Brook (United States)
Lihong Li, SUNY/Stony Brook (United States)
Hongbing Lu, SUNY/Stony Brook (United States)
Dongqing Chen, SUNY/Stony Brook (United States)
Zengrong Liang, SUNY/Stony Brook (United States)

Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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