Abstract
Brain tumour segmentation is a challenging task to perform from the brain MRI image. This is because the different parts of the brain have complex structures. The main objective of image segmentation is to segment the image into parts so that it’s easier to identify the tumour using the classification algorithm. In this paper, a simple linear iterative clustering (SLIC) segmentation algorithm is presented for the segmentation of brain MRI images. This algorithm categorizes the image into different superpixels, which are formed based on the pixel positions and spatial intensity value. However, the superpixels are combined with the neighbouring superpixels and form large regions for merging similar regions. In fast superpixel fusion, the pixel values in each superpixel are replaced by the average pixel value. Find the unique pixel values in the superpixels and cluster them based on the average pixel value. This results in superpixels being grouped into the background with average pixel value zero, grey matter with average pixel values less than two hundred and more than zero, and tumour and skull with average pixel values higher than two hundred. This operation fuses the superpixels at a faster rate and produces the fused superpixels for tumour classification.
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Snehalatha, Patil, S.R. (2023). An Improved Brain Tumour Detection and Classification Using SLIC Superpixel Fusion, Deep Learning and Linear Neighbourhood Semantic Segmentation. In: Kumar, S., Hiranwal, S., Purohit, S., Prasad, M. (eds) Proceedings of International Conference on Communication and Computational Technologies. ICCCT 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-3485-0_67
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DOI: https://doi.org/10.1007/978-981-99-3485-0_67
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