9 May 2002 Unsupervised MRI segmentation with spatial connectivity
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Magnetic Resonance Imaging (MRI) offers a wealth of information for medical examination. Fast, accurate and reproducible segmentation of MRI is desirable in many applications. We have developed a new unsupervised MRI segmentation method based on k-means and fuzzy c-means (FCM) algorithms, which uses spatial constraints. Spatial constraints are included by the use of a Markov Random Field model. The result of segmentation with a four-neighbor Markov Random Field model applied to multi-spectral MRI (5 images including one T1-weighted image, one Proton Density image and three T2-weighted images) in different noise levels is compared to the segmentation results of standard k-means and FCM algorithms. This comparison shows that the proposed method outperforms previous methods.
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Mohammad Mehdi Khalighi, Mohammad Mehdi Khalighi, Hamid Soltanian-Zadeh, Hamid Soltanian-Zadeh, Caro Lucas, Caro Lucas, } "Unsupervised MRI segmentation with spatial connectivity", Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); doi: 10.1117/12.467147; https://doi.org/10.1117/12.467147

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