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10 March 2006 A novel strategy for segmentation of magnetic resonance (MR) images corrupted by intensity inhomogeneity artifacts
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Abstract
Magnetic resonance images are often corrupted by intensity inhomogeneity (i.e., bias field effects), which manifests itself as slow intensity variations over the image domain. Such shading artifacts must be corrected before performing computerized analyses such as intensity-based segmentation and quantitative analysis. In this paper, we present a novel strategy in the fuzzy c-means (FCM) framework that simultaneously estimates the bias field while segmenting the image. An additive field term that models the bias field is incorporated into the FCM objective function. We propose a new term based on the spectral parameterization (i.e., wavelet coefficients) of the bias field that serves as a regularizer to enforce the smoothness of the estimated bias field. We also introduce a second regularization term that causes the labeling of each pixel to be influenced by its immediate neighborhood pixels. The latter regularization term renders the algorithm less sensitive to noise. We show that the novel objective functional could be optimized efficiently using an iterative process. The efficacy of the algorithm is demonstrated on synthesized images as well as on clinical breast MR images. With the synthesize images, segmentation accuracy using standard FCM is 89.07% while segmentation accuracy with the proposed algorithm is 99.95%.
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Weijie Chen and Maryellen L. Giger "A novel strategy for segmentation of magnetic resonance (MR) images corrupted by intensity inhomogeneity artifacts", Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61441C (10 March 2006); doi: 10.1117/12.653602; https://doi.org/10.1117/12.653602
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