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Knowledge based fuzzy c-means method for rapid brain tissues segmentation of magnetic resonance imaging scans with CUDA enabled GPU machine

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Abstract

Fuzzy C-Means (FCM) plays a major role in brain tissue segmentation. The proposed method aims to implements rapid brain tissue segmentation from MRI human head scans using FCM in CPU and GPU. This method is known as FCM-GENIUS. This paper presents three novel steps to enrich the performance of conventional FCM algorithm in CPU. There are region of interest (ROI) selection, knowledge based initialization and knowledge based optimization. The ROI selection is a preprocessing step contains brain extraction and bounding box processes. An automatic knowledge based initialization to FCM algorithm using histogram smoothing for centroids selection from middle slice of the given MRI brain volume. Optimization helps to improve the computation speed up of FCM algorithm using MRI slice adjacency property. The materials used for the proposed work are gathering from internet brain segmentation repository (IBSR). The accuracy of segmentation also compared with traditional and existing methods. The proposed method yield equal segmentation accuracy compared with existing methods but reduces the segmentation time considerably up to seven times and average number of iterations up to three times. In addition, parallel FCM implements in GPU machine and the performance was compared with the conventional FCM in CPU. The single instruction multiple data (SIMD) model was used with the hybrid CPU–GPU implementation in the GPU machine to accelerate the medical image segmentation.

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Acknowledgements

We gratefully acknowledge the support of NVIDIA Corporation Private Ltd., USA with the donation of the QUADRO K5000 GPU used for this research.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, College of Engineering, Dhofar University, Salalah, Oman

    Prajoona Valsalan

  2. Department of Computer Applications, School of Computing, Kalasalingam Academy of Research and Education (Deemed To Be University), Virudhunagar, India

    P. Sriramakrishnan, S. Ramkumar & A. Robert Singh

  3. Saveetha School of Engineering, Saveetha Institute of Medical &Technical Sciences (Deemed To Be University), Chennai, India

    S. Sridhar, G. Charlyn Pushpa Latha & A. Priya

  4. Makeit Technologies (Center for Industrial Research), Coimbatore, India

    T. Rajendran

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  1. Prajoona Valsalan
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  2. P. Sriramakrishnan
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  3. S. Sridhar
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  4. G. Charlyn Pushpa Latha
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  5. A. Priya
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  6. S. Ramkumar
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  7. A. Robert Singh
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  8. T. Rajendran
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Correspondence to T. Rajendran.

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Valsalan, P., Sriramakrishnan, P., Sridhar, S. et al. Knowledge based fuzzy c-means method for rapid brain tissues segmentation of magnetic resonance imaging scans with CUDA enabled GPU machine. J Ambient Intell Human Comput (2020). https://doi.org/10.1007/s12652-020-02132-6

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  • DOI: https://doi.org/10.1007/s12652-020-02132-6

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