Segmentation of Brain Tumor Images using Hybrid Clustering Technique
Keywords:
Medical image segmentation; Brain tumor segmentation; K-means clustering; Fuzzy C-means; Expectation MaximizationAbstract
Image segmentation refers to the process of partitioning an image into mutually exclu-sive regions. It can be considered as the most essential and crucial process for facilitating the delin-eation, characterization, and visualization of regions of interest in any medical image. Despite intensive research, segmentation remains a challenging problem due to the diverse image content, cluttered objects, occlusion, image noise, non-uniform object texture, and other factors. There are many algorithms and techniques available for image segmentation but still there needs to develop an efficient, fast technique of medical image segmentation.
References
- Christe SA, Malathy K, Kandaswamy A. Improved hybrid segmentation of brain MRI tissue and tumor using statistical ICTACT J Image Video Process 2010;1(1):34-49.
- Bandhyopadhyay SK, Paul TU. Automatic segmentation of brain tumour from multiple images of brain MRI. Int J Appl Innovat Eng Manage (IJAIEM) 2013;2(1):240-8.
- Patel J, Doshi K. A study of segmentation methods for detection of tumor in brain MRI. Adv Electron Electr Eng 2014;4(3):279-84.
- Dass R, Priyanka, Devi S. Image segmentation techniques. Int J Electron Commun Technol 2012;3(1):66-70.
- Tatiraju S, Mehta A. Image Segmentation using k means clustering, EM and normalized Cuts, University Of California Irvine, technical report.
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