International Journal of Engineering and Manufacturing (IJEM)
IJEM Vol. 15, No. 3, 8 Jun. 2025
Cover page and Table of Contents: PDF (size: 808KB)
Three Dimensional Rapid Brain Tissue Segmentation with Parallel K-Means Clustering Using Graphics Processing Units
PDF (808KB), PP.18-31
Views: 0 Downloads: 0
Author(s)
Kalaichelvi N.
1
Sriramakrishnan P.
2,*
Kalaiselvi T
3
Saleem Raja A.
4
Index Terms
K-Means Clustering, Virtual Reality, Parallel K-Means, Tissue Segmentation, 3D Segmentation
Abstract
Virtual reality plays a major role in medicine in the aspect of diagnostics and treatment planning. From the diagnostics perspective, automated methods yields the segmented results into virtual environment which will helps the physician to take accurate decisions on time. Virtual reality of 3D brain tissue segmentation helps to diagnostic the brain related diseases like alzheimer's disease, brain malformations, brain tumors, cerebellar disorders and etc. The work proposed a fully automatic histogram-based self-initializing K-Means (HBSKM) algorithm is performed on compute unified device architecture (CUDA) enabled GPU (QudroK5000) machine to segmenting the human brain tissue. Number of clusters (K) and initial centroids (C) automatically calculated from the mid image from the volume through Gaussian smoothening technique. The experimental dataset was collected from internet brain segmentation repository (IBSR) in segmenting the three major tissues such as grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) to experiment the efficiency of the present parallel K-Means algorithm. Computation time is calculated between the homogenous and heterogeneous environment of CPU and GPU for HBSKM algorithm. This proposed work achieved 6× speedup folds while heterogeneous CPU and GPU implementation and 3.5× speedup folds achieved with homogenous GPU implementation. Finally, volume of segmented brain tissue results was presented in virtual 3D and also compared with ground truth results.
Cite This Paper
Kalaichelvi N., Sriramakrishnan P., Kalaiselvi T., Saleem Raja A., "Three Dimensional Rapid Brain Tissue Segmentation with Parallel K-Means Clustering Using Graphics Processing Units", International Journal of Engineering and Manufacturing (IJEM), Vol.15, No.3, pp. 18-31, 2025. DOI:10.5815/ijem.2025.03.02
Reference
[1]Despotovic, I., Goossens, B., & Philips, W. (2015). MRI segmentation of the human brain: challenges, methods, and applications. Computational and mathematical methods in medicine, 2015.
[2]Dora, L., Agrawal, S., Panda, R., & Abraham, A. (2017). State-of-the-art methods for brain tissue segmentation: A review. IEEE reviews in biomedical engineering, 10, 235-249.
[3]Sriramakrishnan, P., Kalaiselvi, T., & Rajeswaran, R. (2019). Modified local ternary patterns technique for brain tumour segmentation and volume estimation from mri multi-sequence scans with gpucuda machine. Biocybernetics and Biomedical Engineering, 39(2), 470-487.
[4]Raghavendra, U., Gudigar, A., Paul, A., Goutham, T. S., Inamdar, M. A., Hegde, A., ... & Acharya, U. R. (2023). Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives. Computers in Biology and Medicine, 107063.
[5]Khalili, N., Lessmann, N., Turk, E., Claessens, N., de Heus, R., Kolk, T., ... & Išgum, I. (2019). Automatic brain tissue segmentation in fetal MRI using convolutional neural networks. Magnetic resonance imaging, 64, 77-89.
[6]Ito, R., Nakae, K., Hata, J., Okano, H., & Ishii, S. (2019). Semi-supervised deep learning of brain tissue segmentation. Neural Networks, 116, 25-34.
[7]Zhang, F., Breger, A., Cho, K. I. K., Ning, L., Westin, C. F., O’Donnell, L. J., & Pasternak, O. (2021). Deep learning based segmentation of brain tissue from diffusion MRI. NeuroImage, 233, 117934.
[8]Kong, Y., Chen, X., Wu, J., Zhang, P., Chen, Y., & Shu, H. (2018). Automatic brain tissue segmentation based on graph filter. BMC Medical Imaging, 18, 1-8.
[9]Krishnapriya, S., & Karuna, Y. (2023). A survey of deep learning for MRI brain tumor segmentation methods: Trends, challenges, and future directions. Health and Technology, 13(2), 181-201.
[10]Kalaiselvi, T., Sriramakrishnan, P., &Somasundaram, K. (2018). Performance of Medical Image Processing Algorithms Implemented in CUDA running on GPU based Machine. International Journal of Intelligent Systems and Applications, 11(1), 58.
[11]AlZu’bi, S., Shehab, M., Al-Ayyoub, M., Jararweh, Y., & Gupta, B. (2020). Parallel implementation for 3d medical volume fuzzy segmentation. Pattern Recognition Letters, 130, 312-318.
[12]Jaume, S., Knobe, K., Newton, R. R., Schlimbach, F., Blower, M., & Reid, R. C. (2011). A multiscale parallel computing architecture for automated segmentation of the brain connectome. IEEE Transactions on Biomedical Engineering, 59(1), 35-38.
[13]Saxena, S., & Shama, S. (2018, December). Brain tumor segmentation by texture feature extraction with the parallel implementation of fuzzy c-means using CUDA on GPU. In 2018 fifth international conference on parallel, distributed and grid computing (PDGC) (pp. 580-585). IEEE.
[14]Farivar, R., Rebolledo, D., Chan, E., & Campbell, R. H. (2008, July). A Parallel Implementation of K-Means Clustering on GPUs. In Pdpta (Vol. 13, No. 2, pp. 212-312).
[15]Thiruvenkadam, K., Nagarajan, K., & Padmanaban, S. (2021). An automatic self‐initialized clustering method for brain tissue segmentation and pathology detection from magnetic resonance human head scans with graphics processing unit machine. Concurrency and Computation: Practice and Experience, 33(6), e6084.
[16]Kucukyilmaz, T., & University of Turkish Aeronautical Association. (2014). Parallel k-means algorithm for shared memory multiprocessors. Journal of Computer and Communications, 2(11), 15.
[17]Borlea, I. D., Precup, R. E., Dragan, F., &Borlea, A. B. (2018, May). Parallel Implementation of K-Means Algorithm Using MapReduce Approach. In 2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI) (pp. 000075-000080). IEEE.
[18]Baydoun, M., Dawi, M., &Ghaziri, H. (2016, July). Enhanced parallel implementation of the K-Means clustering algorithm. In 2016 3rd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA) (pp. 7-11). IEEE.
[19]Sriramakrishnan, P., Kalaiselvi, T., Somasundaram, K., & Rajeswaran, R. (2019). A rapid knowledge‐based partial supervision fuzzy c‐means for brain tissue segmentation with CUDA‐enabled GPU machine. International Journal of Imaging Systems and Technology, 29(4), 547-560.
[20]Ali, N. A., Cherradi, B., El Abbassi, A., Bouattane, O., &Youssfi, M. (2018). GPU fuzzy c-means algorithm implementations: performance analysis on medical image segmentation. Multimedia Tools and Applications, 77(16), 21221-21243.
[21]Kalaiselvi, T., Kalaichelvi, N., & Sriramakrishnan, P. (2017). Automatic brain tissues segmentation based on self initializing K-means clustering technique. International Journal of Intelligent Systems and Applications, 9(11), 52.
[22]Internet Brain Segmentation Repository. Center for Morphometric Analysis at Massachusetts General Hospital. http://www.cma.mgh.harvard.edu/ibsr/index.html.
[23]Benson, C. C., Lajish, V. L., & Rajamani, K. (2016). A novel skull stripping and enhancement algorithm for the improved brain tumor segmentation using mathematical morphology. International Journal of Image, Graphics and Signal Processing, 8(7), 59.
[24]Somasundaram K, Kalaiselvi T. Automatic Brain Extraction Methods for T1 Magnetic Resonance Images using Region Labeling and Morphological Operations. Comput Biol Med. 2011;41:716–725.
[25]Kalaiselvi, T., Sriramakrishnan, P., & Nirmala, J. (2017). An investigation on suitable distance measure to FCM method for brain tissue segmentation. Computational Methods, Communication Techniques and Informatics.
[26]Narendran, P., Kumar, V. N., & Somasundaram, K. (2012). 3D Brain Tumors and internal brain structures segmentation in mr images. International Journal of Image, Graphics and Signal Processing, 4(1), 35.