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A new method for skull stripping in brain MRI using multistable cellular neural networks

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Abstract

This study proposes a new method on “detecting brain region in MRI data”. This task is generally named as “skull stripping” in the literature. The algorithm is developed by using the cellular neural networks (CNNs) and multistable CNN structures. It also includes a contrast enhancement and noise reduction algorithm. The algorithm is named as multistable cellular neural network on MRI for skull stripping (mCNN-MRI-SS). Three different case studies are performed for measuring the success of the algorithm. Also a fourth case study is performed to evaluate the supporting algorithm, the CEULICA. First two evaluations are performed by using well-known MIDAS-NAMIC and Brainweb databases, which are properly organized Talairach-compatible databases. The third database was obtained from the research and application hospital of Necmettin Erbakan University Meram Faculty of Medicine. These MRI data were not Talairach-compatible and less sampled. The algorithm achieved 0.595 Jaccard, 0.744 Dice, 0.0344 TPF and 0.383 TNF mean values with the Brainweb T1-weighted images and 0.837 Jaccard, 0.898 Dice, 0.0124 TPF and 0.1511 TNF mean values with the MIDAS-NAMIC T2-weighted images. The algorithm achieved 0.8297 Jaccard, 0.9012 Dice, 0.0951 TPF and 0.1225 TNF mean values and achieved with the obtained data the best values among the other algorithms. As a result, it can be claimed that algorithm performs best with the non-Talairach-compatible MRI data due to its nature of performing at cellular level.

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Acknowledgements

The MRI brain images of the NAMIC database used in this paper were collected and made available by the CASILab at the University of North Carolina at Chapel Hill and were distributed by the MIDAS Data Server at Kitware, Inc. The data were obtained from Necmettin Erbakan University Meram Faculty of Medicine, according to ethical committee of clinical researches decision No. 2011/235.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Selcuk University, Konya, Turkey

    Burak Yilmaz & Akif Durdu

  2. Department of Radiology, Meram Medical Faculty, Necmettin Erbakan University, Konya, Turkey

    Ganime Dilek Emlik

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  1. Burak Yilmaz
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Correspondence to Burak Yilmaz.

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Yilmaz, B., Durdu, A. & Emlik, G.D. A new method for skull stripping in brain MRI using multistable cellular neural networks. Neural Comput & Applic 29, 79–95 (2018). https://doi.org/10.1007/s00521-016-2834-2

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