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Fast uncertainty-guided fuzzy C-means segmentation of medical images

: Al-Taie, A.; Hahn, H.K.; Linsen, L.


Linsen, L.:
Visualization in Medicine and Life Sciences III : Towards Making an Impact
Cham: Springer International Publishing, 2016 (Mathematics and visualization)
ISBN: 978-3-319-24521-8 (Print)
ISBN: 978-3-319-24523-2 (Online)
Aufsatz in Buch
Fraunhofer MEVIS ()

Image segmentation is a crucial step of themedical visualization pipeline. In this paper, we present a novel fast algorithm for modified fuzzy c-means segmentation of MRI data. The algorithm consists of two steps, which are executed as two iterations of a fuzzy c-means approach: the first iteration is a standard fuzzy c-means (FCM) iteration, while the second iteration is our modified FCM iteration with misclassification correction. In the second iteration, we use the classification probability vectors (uncertainties) of the neighbor pixels found by the first iteration to confirm or correct the classification decision of the current pixel. The application of the proposed algorithm on synthetic data, simulated MRI data, and real MRI data show that our algorithm is insensitive to different types of noise and outperforms the standard FCM and several versions of modified FCM algorithms in terms of accuracy and speed.