Intensity inhomogeneity compensation and tissue segmentation for magnetic resonance imaging with noise-suppressed multiplicative intrinsic component optimization

Huaipeng Dong; Qi Zhang; Jun Shi

doi:10.1117/1.OE.56.12.123103

9 December 2017 Intensity inhomogeneity compensation and tissue segmentation for magnetic resonance imaging with noise-suppressed multiplicative intrinsic component optimization

Huaipeng Dong, Qi Zhang, Jun Shi

Author Affiliations +

Optical Engineering, Vol. 56, Issue 12, 123103 (December 2017). https://doi.org/10.1117/1.OE.56.12.123103

Abstract

Magnetic resonance (MR) images suffer from intensity inhomogeneity. Segmentation-based approaches can simultaneously achieve both intensity inhomogeneity compensation (IIC) and tissue segmentation for MR images with little noise, but they often fail for images polluted by severe noise. Here, we propose a noise-robust algorithm named noise-suppressed multiplicative intrinsic component optimization (NSMICO) for simultaneous IIC and tissue segmentation. Considering the spatial characteristics in an image, an adaptive nonlocal means filtering term is incorporated into the objective function of NSMICO to decrease image deterioration due to noise. Then, a fuzzy local factor term utilizing the spatial and gray-level relationship among local pixels is embedded into the objective function to reach a balance between noise suppression and detail preservation. Experimental results on synthetic natural and MR images with various levels of intensity inhomogeneity and noise, as well as in vivo clinical MR images, have demonstrated the effectiveness of the NSMICO and its superiority to three competing approaches. The NSMICO could be potentially valuable for MR image IIC and tissue segmentation.

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Huaipeng Dong, Qi Zhang, and Jun Shi "Intensity inhomogeneity compensation and tissue segmentation for magnetic resonance imaging with noise-suppressed multiplicative intrinsic component optimization," Optical Engineering 56(12), 123103 (9 December 2017). https://doi.org/10.1117/1.OE.56.12.123103

Received: 30 July 2017; Accepted: 15 November 2017; Published: 9 December 2017

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KEYWORDS

Image segmentation

Magnetic resonance imaging

Tissues

Digital filtering

Image filtering

Optical engineering

Image processing algorithms and systems

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