Fuzzy C-means clustering algorithm applied in computed tomography images of patients with intracranial hemorrhage

In recent years, intracerebral hemorrhage (ICH) has garnered significant attention as a severe cerebrovascular disorder. To enhance the accuracy of ICH detection and segmentation, this study proposed an improved fuzzy C-means (FCM) algorithm and performed a comparative analysis with both traditional...

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Vydáno v:	Frontiers in neuroinformatics Ročník 18; s. 1440304
Hlavní autoři:	Zhang, Lintao, Song, Dewen, Qiu, Huiying, Ye, Lin, Xu, Zengliang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland Frontiers Media S.A 23.10.2024
Témata:	computed tomography (CT) images convolutional neural network (CNN) fuzzy C-means clustering (FCM) algorithm image segmentation intracranial hemorrhage (ICH) Neuroscience fuzzy C-means clustering (FCM) algorithm convolutional neural network (CNN) image segmentation intracranial hemorrhage (ICH) computed tomography (CT) images
ISSN:	1662-5196, 1662-5196
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Shrnutí:	In recent years, intracerebral hemorrhage (ICH) has garnered significant attention as a severe cerebrovascular disorder. To enhance the accuracy of ICH detection and segmentation, this study proposed an improved fuzzy C-means (FCM) algorithm and performed a comparative analysis with both traditional FCM and advanced convolutional neural network (CNN) algorithms. Experiments conducted on the publicly available CT-ICH dataset evaluated the performance of these three algorithms in predicting ICH volume. The results demonstrated that the improved FCM algorithm offered notable improvements in computational time and resource consumption compared to the traditional FCM algorithm, while also showing enhanced accuracy. However, it still lagged behind the CNN algorithm in areas such as feature extraction, model generalization, and the ability to handle complex image structures. The study concluded with a discussion of potential directions for further optimizing the FCM algorithm, aiming to bridge the performance gap with CNN algorithms and provide a reference for future research in medical image processing.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Shailesh Appukuttan, UMR7289 Institut de Neurosciences de la Timone (INT), France Reviewed by: Riccardo De Feo, Charles River Discovery Research Services, Finland Vignayanandam Ravindernath Muddapu, Ecole polytechnique fédérale de Lausanne (EPFL), Switzerland
ISSN:	1662-5196 1662-5196
DOI:	10.3389/fninf.2024.1440304