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Fast Uncertainty-Guided Fuzzy C-Means Segmentation of Medical Images

  • Ahmed Al-Taie
  • Horst K. Hahn
  • Lars Linsen
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

Image segmentation is a crucial step of the medical 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. In fact, our algorithm can easily be combined with many modified FCM algorithms to improve their segmentation result while reducing the computation costs (using two FCM iterations only). An optional simple post-processing step can further improve the segmentation result by correcting isolated misclassified pixels. We also show that our algorithm reduces the uncertainty in the segmentation result, by using recently proposed uncertainty estimation and visualization tools.

Keywords

Synthetic Image Segmentation Accuracy Noisy Pixel Fuzzy Partition Matrix Neighborhood Majority 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Jacobs UniversityBremenGermany
  2. 2.Baghdad UniversityBaghdadIraq
  3. 3.Fraunhofer MEVISBremenGermany

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