Walsh Hadamard Transform for Simple Linear Iterative Clustering (SLIC) Superpixel Based Spectral Clustering of Multimodal MRI Brain Tumor Segmentation
| Autor: | G. G. Lakshmi Priya, M. Angulakshmi |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry 0206 medical engineering Biomedical Engineering Biophysics Pattern recognition 02 engineering and technology 020601 biomedical engineering Spectral clustering 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Hausdorff distance Hadamard transform Simple (abstract algebra) Core (graph theory) Graph (abstract data type) Segmentation Artificial intelligence Cluster analysis business |
| Zdroj: | IRBM. 40:253-262 |
| ISSN: | 1959-0318 |
| DOI: | 10.1016/j.irbm.2019.04.005 |
| Popis: | The automated brain tumor segmentation methods are challenging due to the diverse nature of tumors. Recently, the graph based spectral clustering method is utilized for brain tumor segmentation to make high-quality segmentation output. In this paper, a new Walsh Hadamard Transform (WHT) texture for superpixel based spectral clustering is proposed for segmentation of a brain tumor from multimodal MRI images. First, the selected kernels of WHT are utilized for creating texture saliency maps and it becomes the input for the Simple Linear Iterative Clustering (SLIC) algorithm, to generate more precise texture based superpixels. Then the texture superpixels become nodes in the graph of spectral clustering for segmenting brain tumors of MRI images. Finally, the original members of superpixels are recovered to represent Complete Tumor (CT), Tumor Core (TC) and Enhancing Tumor (ET) tissues. The observational results are taken out on BRATS 2015 datasets and evaluated using the Dice Score (DS), Hausdorff Distance (HD) and Volumetric Difference (VD) metrics. The proposed method produces competitive results than other existing clustering methods. |
| Databáze: | OpenAIRE |
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