Fuzzy farthest point first method for MRI brain image clustering
| Autor: | Mohammed Debakla, Khaled Benmeriem, Khalifa Djemal, Mohamed Salem |
|---|---|
| Přispěvatelé: | Faculty of Science Exact, University of Mustapha Stambouli Mascara, Informatique, BioInformatique, Systèmes Complexes (IBISC), Université d'Évry-Val-d'Essonne (UEVE) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Fuzzy clustering
Jaccard index medicine.diagnostic_test business.industry Computer science 020206 networking & telecommunications Magnetic resonance imaging Pattern recognition 02 engineering and technology Image segmentation Fuzzy logic Signal Processing 0202 electrical engineering electronic engineering information engineering medicine A priori and a posteriori 020201 artificial intelligence & image processing Segmentation Computer Vision and Pattern Recognition Artificial intelligence Electrical and Electronic Engineering business Cluster analysis [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing Software |
| Zdroj: | IET Image Processing IET Image Processing, Institution of Engineering and Technology, 2019, 13 (13), pp.2395--2400. ⟨10.1049/iet-ipr.2018.6618⟩ IET Image Processing, 2019, 13 (13), pp.2395--2400. ⟨10.1049/iet-ipr.2018.6618⟩ |
| ISSN: | 1751-9659 1751-9667 |
| DOI: | 10.1049/iet-ipr.2018.6618⟩ |
| Popis: | International audience; Image clustering is considered amongst the most important tasks in medical image analysis and it is regularly required as a starter and vital stage in the computer-aided medical image process. In brain magnetic resonance imaging (MRI) analysis, image clustering is regularly used for estimating and visualising the brain anatomical structures, to detect pathological regions and to guide surgical procedures. This study presents a new method for MRI brain images clustering based on the farthest point first algorithm and fuzzy clustering techniques without using any a priori information about the clusters number. The algorithm has been approved against both simulated and clinical magnetic resonance images and it has been compared with the fourth clustered algorithms. Results demonstrate that the proposed algorithm has given reasonable segmentation of white matter, grey matter and cerebrospinal fluid from MRI data, which is superior in preserving image details and segmentation accuracy compared with the other four algorithms giving more than 91% in Jaccard similarity. |
| Databáze: | OpenAIRE |
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