Novel Hybrid Optimized Clustering Schemes with Genetic Algorithm and PSO for Segmentation and Classification of Articular Cartilage Loss from MR Images

Jan Kubicek (), Alice Varysova, Martin Cerny, Jiri Skandera, David Oczka, Martin Augustynek and Marek Penhaker
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Jan Kubicek: Department of Cybernetics and Biomedical Engineering, VŠB—Technical University of Ostrava, 17 Listopadu 2172/15, Poruba, 708 00 Ostrava, Czech Republic
Alice Varysova: Department of Cybernetics and Biomedical Engineering, VŠB—Technical University of Ostrava, 17 Listopadu 2172/15, Poruba, 708 00 Ostrava, Czech Republic
Martin Cerny: Department of Cybernetics and Biomedical Engineering, VŠB—Technical University of Ostrava, 17 Listopadu 2172/15, Poruba, 708 00 Ostrava, Czech Republic
Jiri Skandera: Department of Cybernetics and Biomedical Engineering, VŠB—Technical University of Ostrava, 17 Listopadu 2172/15, Poruba, 708 00 Ostrava, Czech Republic
David Oczka: Department of Cybernetics and Biomedical Engineering, VŠB—Technical University of Ostrava, 17 Listopadu 2172/15, Poruba, 708 00 Ostrava, Czech Republic
Martin Augustynek: Department of Cybernetics and Biomedical Engineering, VŠB—Technical University of Ostrava, 17 Listopadu 2172/15, Poruba, 708 00 Ostrava, Czech Republic
Marek Penhaker: Department of Cybernetics and Biomedical Engineering, VŠB—Technical University of Ostrava, 17 Listopadu 2172/15, Poruba, 708 00 Ostrava, Czech Republic

Mathematics, 2023, vol. 11, issue 4, 1-26

Abstract: Medical image segmentation plays an indispensable role in the identification of articular cartilage, tibial and femoral bones from magnetic resonance imaging (MRI). There are various image segmentation strategies that can be used to identify the knee structures of interest. Among the most popular are the methods based on non-hierarchical clustering, including the algorithms K-means and fuzzy C-means (FCM). Although these algorithms have been used in many studies for regional image segmentation, they have two essential drawbacks that limit their performance and accuracy of segmentation. Firstly, they rely on a precise selection of initial centroids, which is usually conducted randomly, and secondly, these algorithms are sensitive enough to image noise and artifacts, which may deteriorate the segmentation performance. Based on such limitations, we propose, in this study, two novel alternative metaheuristic hybrid schemes: non-hierarchical clustering, driven by a genetic algorithm, and Particle Swarm Optimization (PSO) with fitness function, which utilizes Kapur’s entropy and statistical variance. The goal of these optimization elements is to find the optimal distribution of centroids for the knee MR image segmentation model. As a part of this study, we provide comprehensive testing of the robustness of these novel segmentation algorithms upon the image noise generators. This includes Gaussian, Speckle, and impulsive Salt and Pepper noise with dynamic noise to objectively report the robustness of the proposed segmentation strategies in contrast with conventional K-means and FCM. This study reveals practical applications of the proposed algorithms for articular cartilage extraction and the consequent classification performance of early osteoarthritis based on segmentation models and convolutional neural networks (CNN). Here, we provide a comparative analysis of GoogLeNet and ResNet 18 with various hyperparameter settings, where we achieved 99.92% accuracy for the best classification configuration for early cartilage loss recognition.

Keywords: medical image segmentation; articular cartilage; non-hierarchical clustering; K-means; FCM; genetic algorithms; PSO (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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