A Novel Approach based on PSO Optimized K-Means in MRI Brain Image Segmentation

Mano, Abhisha

doi:10.36227/techrxiv.12310100.v2

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…To assess the efficiency and precision of the proposed method in segmenting the MRI dataset, accuracy was employed as an evaluation metric. The proposed method was compared to that of the PSO algorithm [21], which has demonstrated satisfactory performance in this domain. Accuracy is computed by:…”

Section: Discussionmentioning

confidence: 99%

“…In various data clustering scenarios, combining GAs with clustering techniques, such as k-means, yields more interpretable results than using a standalone clustering algorithm. The GKMC technique was initially developed to achieve a globally optimal partition in the field of pattern recognition, and it has demonstrated its ability to converge to an optimal solution [21].…”

Section: Introductionmentioning

confidence: 99%

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Dominant Gray Level-based Genetic K-means Clustering Algorithm for MRI Image Segmentation

Khaleel,

Mohammed,

Qays

2024

Eng. Technol. Appl. Sci. Res.

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In this paper, a method and fresh results associated with medical image segmentation of brain Magnetic Resonance Imaging (MRI) scans are presented. Gray-converted segmentation and Genetic Algorithm (GA) are utilized along with unsupervised k-means classification. The image segmentation employed indicates the tissue type or the anatomical structure of each pixel. The cluster centroid initialization is performed by GA. GA offers efficient search processes (selection, crossover, and mutation), suited to determine global optima regarding centroid problems. As a result, this research offers more accurate, reliable, and efficient image segmentation for MRI, by improving the k-means algorithm with GA. The results indicate that the accuracy obtained from the proposed method is at least 3.5% higher than the PSO algorithm in this matter.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dominant Gray Level-based Genetic K-means Clustering Algorithm for MRI Image Segmentation

Khaleel,

Mohammed,

Qays

2024

Eng. Technol. Appl. Sci. Res.

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“…The implementation of Clustering and PSO combination coding was carried out by Augusto Luis Ballardini, referring to Merwe et al (Ballardini, 2018). The success of PSO in producing more optimal clustering is confirmed by research by Abhinsha (Mano, 2020) and Rudi (Hariyanto and Zoqi Sarwani, 2021). The combination of K-Means Clustering with PSO in other research fields has also been successful in image-based identification of medicinal plants.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Clustering of Indonesian Text Data Using Particle Swarm Optimization Algorithm: A Case Study of the Quran Translation

Wahyudi,

Fatwanto

2024

telematika.

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The Quran considered the holy book for Muslims, contains scientific and historical facts affirming Islam's truth, beauty, and influence on human life. Consequently, the Quran text and its translations are valuable sources for text mining research, particularly for studying the interrelationship of its verses. One approach to grouping objects using certain algorithms is clustering, with K-Means Clustering being a prominent example. However, clustering results are often suboptimal due to the random selection of centroids. To address this, the study proposes using the Particle Swarm Optimization (PSO) algorithm, which selects centroids based on PSO results. The hybrid PSO algorithm initiates a single iteration of the K-means algorithm. It concludes either upon reaching the maximum iteration limit or when the average shift in the center of the mass vector falls below 0.0001. Evaluation of the clustering results from the three models indicates that the K-Means algorithm produced the lowest Sum of Squared Error (SSE) value of 1032.19. Additionally, the hybrid PSO algorithm generated the highest Silhouette value of 0.258 and the lowest quantization value of 0.00947. Further evaluation using a confusion matrix showed that K-Means clustering had an accuracy rate of 81.7%, K-Means with PSO had 82.5%, and the combination of K-Means with hybrid PSO yielded the highest accuracy rate of 91.1% among the three grouping model.

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A Novel Approach based on PSO Optimized K-Means in MRI Brain Image Segmentation

Abstract: A brain image segmentation by an optimization technique.<br>

Cited by 2 publications

References 6 publications

Dominant Gray Level-based Genetic K-means Clustering Algorithm for MRI Image Segmentation

Dominant Gray Level-based Genetic K-means Clustering Algorithm for MRI Image Segmentation

Optimizing Clustering of Indonesian Text Data Using Particle Swarm Optimization Algorithm: A Case Study of the Quran Translation

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