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. 2024 Oct 23:18:1440304.
doi: 10.3389/fninf.2024.1440304. eCollection 2024.

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

Lintao Zhang  1 , Dewen Song  1 , Huiying Qiu  2 , Lin Ye  1 , Zengliang Xu  1
Affiliations

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

Lintao Zhang et al. Front Neuroinform. .

Abstract

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.

Keywords: computed tomography (CT) images; convolutional neural network (CNN); fuzzy C-means clustering (FCM) algorithm; image segmentation; intracranial hemorrhage (ICH).

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow chart for segmentation of algorithm.
Figure 2
Figure 2
Flow chart of data augmentation.
Figure 3
Figure 3
Cranial CT image clustering operation result diagram. A was the brain source image, B was the result of FCM fuzzy clustering, and C was the result processed by the preliminary algorithm.
Figure 4
Figure 4
The result of the improved brain CT image clustering operation. A was the result of adding the spatial algorithm, B showed the result of the improved algorithm, and C showed the result of the analysis of the singular point by the improved algorithm.
Figure 5
Figure 5
Comparison of lesion segmentation of cranial CT. A was the intracranial CT image, B showed the FCM clustering result, and C showed the segmentation result of the algorithm.
Figure 6
Figure 6
Comparison of lesion area. “*” indicated compared to FCM group, p < 0.05; “#” indicated compared to improved FCM group, p < 0.05.
Figure 7
Figure 7
Comparison on difference between three algorithms.
Figure 8
Figure 8
Cranial CT image clustering operation result diagram. A was the brain source image, B was the result of FCM fuzzy clustering, and C was the result processed by the preliminary algorithm, D was the result of adding the spatial algorithm, E showed the result of the improved algorithm, and F showed the result of the analysis of the singular point by the improved algorithm.
See this image and copyright information in PMC

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