Improved Particle Swarm Medical Image Segmentation Algorithm for Decision Making

El-Khatib, Samer; Skobtsov, Y.A.; Rodzin, Sergey

doi:10.1007/978-3-030-32258-8_51

Cited by 9 publications

(2 citation statements)

References 3 publications

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“…Our method harnesses the power of particle swarm optimization (PSO) [22] in conjunction with histogram equalization (HE) [23] with the aim of significantly enhancing the accuracy and robustness of image segmentation in the medical domain. PSO emerges as a promising optimization framework for addressing the intricacies of medical image segmentation [24,25]. Inspired by the collective behavior of swarming particles, PSO excels at finding optimal solutions in complex, multidimensional spaces [26].…”

Section: Introductionmentioning

confidence: 99%

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Saifullah,

Dreżewski

2024

Applied Sciences

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Accurate medical image segmentation is paramount for precise diagnosis and treatment in modern healthcare. This research presents a comprehensive study of the efficacy of particle swarm optimization (PSO) combined with histogram equalization (HE) preprocessing for medical image segmentation, focusing on lung CT scan and chest X-ray datasets. Best-cost values reveal the PSO algorithm’s performance, with HE preprocessing demonstrating significant stabilization and enhanced convergence, particularly for complex lung CT scan images. Evaluation metrics, including accuracy, precision, recall, F1-score/Dice, specificity, and Jaccard, show substantial improvements with HE preprocessing, emphasizing its impact on segmentation accuracy. Comparative analyses against alternative methods, such as Otsu, Watershed, and K-means, confirm the competitiveness of the PSO-HE approach, especially for chest X-ray images. The study also underscores the positive influence of preprocessing on image clarity and precision. These findings highlight the promise of the PSO-HE approach for advancing the accuracy and reliability of medical image segmentation and pave the way for further research and method integration to enhance this critical healthcare application.

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Section: Introductionmentioning

confidence: 99%

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Saifullah,

Dreżewski

2024

Applied Sciences

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“…Эволюционные алгоритмы применяются для решения множества практических задач, таких как составление расписаний в промышленности [15,16], оптимизация разводки печатных плат [17][18][19][20], построение систем управления [21][22][23][24][25][26][27], сегментирование изображений [28], а также приближенного решения других задач комбинаторной [29][30][31][32] и вещественной оптимизации [33][34][35].…”

Section: Introductionunclassified

The “One-fifth Rule” with Rollbacks for Self-Adjustment of the Population Size in the (1 + (λ,λ)) Genetic Algorithm

Басин

Буздалов

Шалыто

2020

Model. anal. inf. sist.

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Self-adjustment of parameters can significantly improve the performance of evolutionary algorithms. A notable example is the (1 + (λ,λ)) genetic algorithm, where adaptation of the population size helps to achieve the linear running time on the OneMax problem. However, on problems which interfere with the assumptions behind the self-adjustment procedure, its usage can lead to the performance degradation. In particular, this is the case with the “one-fifth rule” on problems with weak fitness-distance correlation.We propose a modification of the “one-fifth rule” in order to have less negative impact on the performance in the cases where the original rule is destructive. Our modification, while still yielding a provable linear runtime on OneMax, shows better results on linear function with random weights, as well as on random satisfiable MAX-3SAT problems.

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Comparison of Hybrid ACO-k-Means Algorithm and Graph Cut for MRI Images Segmentation

El-Khatib

Skobtsov

Rodzin

et al. 2020

Advances in Intelligent Systems and Computing

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Improved Particle Swarm Medical Image Segmentation Algorithm for Decision Making

Cited by 9 publications

References 3 publications

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

The “One-fifth Rule” with Rollbacks for Self-Adjustment of the Population Size in the (1 + (λ,λ)) Genetic Algorithm

Comparison of Hybrid ACO-k-Means Algorithm and Graph Cut for MRI Images Segmentation

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