| _version_ | 1866902232535924736 |
|---|---|
| author | Journal of Theoretical and Applied Information Technology |
| author_facet | Journal of Theoretical and Applied Information Technology |
| contents | <p>Magnetic Resonance Imaging (MRI) is a well applied method of brain analysis, because of its capability of acquiring detailed anatomical information. Accurate brain MRI segmentation is required for diagnosing brain related disorders. A brain MRI segmentation framework based on K-Means++ clustering and a novel vectorized fuzzy membership computation is proposed as the work that introduces an optimized solution to this problem with additional accuracy and speed. K-Means++ is deployed to initialize cluster centers in order to improve convergence time and quality of the segmentation. The computed vectorized fuzzy membership functions yield additional tissue segmentation for fine classification of tissues (gray matter, white matter and cerebrospinal fluid etc.) that are not provided by a tissue region segmented alone. A combined method based on robust noise reduction through application of Gaussian filtering linked to robust intensity normalization on an image quality basis to remove artifacts based upon noisy images is presented. Small, spurious regions are then removed using post-processing techniques. On benchmark brain MRI datasets, experiments demonstrate the superiority of the optimized segmentation method in terms of both segmentation accuracy and computational efficiency as compared to traditional K Means and Fuzzy C Means (FCM) algorithms, all with robustness against noise. The proposed method achieves 0.21% and 0.52% improvements in accuracy over traditional K-Means and FCM. Proposed method showing significant improvement in Dice Similarity Coefficient (DSC), Jaccard’s Index (JI), precision, recall, F1-score and MSE parameters compared to K-Means and FCM. This contribution makes a computationally efficient and more accurate hybrid segmentation approach to integrate K-means++ and vectorized fuzzy membership computation so as to boost the reliability of brain imaging analyses and clinical decision making.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16965848 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | OPTIMIZED BRAIN MRI SEGMENTATION: K-MEANS++ AND VECTORISED FUZZY MEMBERSHIP COMPUTATION Journal of Theoretical and Applied Information Technology Brain Tumor, Fuzzy C Means, Fuzzy membership functions, K-Means++, MRI Image <p>Magnetic Resonance Imaging (MRI) is a well applied method of brain analysis, because of its capability of acquiring detailed anatomical information. Accurate brain MRI segmentation is required for diagnosing brain related disorders. A brain MRI segmentation framework based on K-Means++ clustering and a novel vectorized fuzzy membership computation is proposed as the work that introduces an optimized solution to this problem with additional accuracy and speed. K-Means++ is deployed to initialize cluster centers in order to improve convergence time and quality of the segmentation. The computed vectorized fuzzy membership functions yield additional tissue segmentation for fine classification of tissues (gray matter, white matter and cerebrospinal fluid etc.) that are not provided by a tissue region segmented alone. A combined method based on robust noise reduction through application of Gaussian filtering linked to robust intensity normalization on an image quality basis to remove artifacts based upon noisy images is presented. Small, spurious regions are then removed using post-processing techniques. On benchmark brain MRI datasets, experiments demonstrate the superiority of the optimized segmentation method in terms of both segmentation accuracy and computational efficiency as compared to traditional K Means and Fuzzy C Means (FCM) algorithms, all with robustness against noise. The proposed method achieves 0.21% and 0.52% improvements in accuracy over traditional K-Means and FCM. Proposed method showing significant improvement in Dice Similarity Coefficient (DSC), Jaccard’s Index (JI), precision, recall, F1-score and MSE parameters compared to K-Means and FCM. This contribution makes a computationally efficient and more accurate hybrid segmentation approach to integrate K-means++ and vectorized fuzzy membership computation so as to boost the reliability of brain imaging analyses and clinical decision making.</p> |
| title | OPTIMIZED BRAIN MRI SEGMENTATION: K-MEANS++ AND VECTORISED FUZZY MEMBERSHIP COMPUTATION |
| topic | Brain Tumor, Fuzzy C Means, Fuzzy membership functions, K-Means++, MRI Image |
| url | https://doi.org/10.5281/zenodo.16965848 |