A Fast K-means Clustering Algorithm for Separation of Brain Tissues in MRI
| Autor: | Djamel Eddine Chouaib Belkhiat, Dalel Jabri, Imane Mehidi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Jaccard index Computational complexity theory medicine.diagnostic_test business.industry Computer science k-means clustering Pattern recognition Magnetic resonance imaging 02 engineering and technology 020901 industrial engineering & automation Histogram 0202 electrical engineering electronic engineering information engineering Median filter medicine 020201 artificial intelligence & image processing Artificial intelligence Cluster analysis business Image histogram |
| Zdroj: | 2020 2nd International Conference on Mathematics and Information Technology (ICMIT). |
| DOI: | 10.1109/icmit47780.2020.9046971 |
| Popis: | Efficient separation of brain tissues in Magnetic Resonance Imaging (MRI) is a very important step for the quantitative diagnosis of brain diseases. To identify important brain regions such as the gray matter (GM), white matter (WM) and the cerebrospinal substance fluid spaces (CSF), we proposed in this paper a new improved K-means algorithm (called HKM: Histogram-based K-Means) based on the image histogram and the median filter. The proposed algorithm is characterized by its ability to segment image faster and robustness in the presence of noise and non-uniform tissues. Moreover, it reduces the computational complexity and improves the performances of K-means algorithm in terms of the Jaccard and Dice Indexes. The obtained results demonstrate that the proposed HKM algorithm requires less times and achieves better results than the standard K-means clustering algorithm. |
| Databáze: | OpenAIRE |
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