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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10252299" target="_blank" >RIV/61989100:27240/23:10252299 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2227-7390/11/4/1027" target="_blank" >https://www.mdpi.com/2227-7390/11/4/1027</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/math11041027" target="_blank" >10.3390/math11041027</a>

Result language

angličtina

Original language name

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

Original language description

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&apos;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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF17_049%2F0008441" target="_blank" >EF17_049/0008441: Innovative therapeutic methods of musculoskeletal system in accident surgery</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Mathematics

ISSN

2227-7390

e-ISSN

2227-7390

Volume of the periodical

11

Issue of the periodical within the volume

4

Country of publishing house

CH - SWITZERLAND

Number of pages

26

Pages from-to

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UT code for WoS article

000941588500001

EID of the result in the Scopus database

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