Abstract
Separating an image into regions according to some criterion is called image segmentation. This paper presents an algorithm that combines the fuzzy k-means (FKM) and fuzzy c-means (FCM) clustering strategies. The proposed algorithm combines the FKM and the FCM algorithms mathematical features, which is referred to as (CFKCM). The FKM and FCM clustering algorithms are adopted to compare the performance and hence evaluate the proposed clustering algorithm. Tests are conducted, and performance parameters are calculated for validation. The comparison and assessment analysis are based on metrics related to the image clustering process, such as the Segmentation Accuracy (SA), Clustering Fitness (CF), and cluster Validity function (V<sub>pc</sub> and V<sub>pe</sub>). A dataset of MR images is used by this research for the application, test, and evaluation of the image clustering. The results for clustering backbone MRI images show that CFKCM algorithm is featured by being more effective, and comparatively independent of the noise, where it can process the
Highlights
The field of image processing involves a number of major topics that are considered as hot research areas
The experimental results show that the CFKCM enhanced algorithmic segmentation in the following attributes; a- the starting number of clusters is almost optimal, b- higher Segmentation Accuracy (SA) even with noisy images, c- robust algorithm that, maintains the structural characteristics of the segmented image, d- higher Clustering Fitness (CF), where the proposed algorithm shows appreciable performance for all sorts of noises, e- the center of the cluster is located in the optimal gravity center of all elements that belongs to the cluster
We took into consideration a number of factors that are adopted in the field for performance measurement
Summary
The field of image processing involves a number of major topics that are considered as hot research areas. The standard FCM computes the memberships based on the Euclidean distance between pixels This affects the clustering performance especially when neighboring pixels are having strong correlation such as the medical MR images. Adopting C-means algorithm for clustering will reveal good results in segmenting noiseless images, but not with noisy images Considering these facts, benefits may be obtained from integrating the two algorithms and having the number of iterations reduced, which affects the execution time and give an accurate detection. Both FCM and FKM algorithms perform the steps illustrated in the flowchart of Figure. The clustering fitness has a higher value when the inter-cluster similarity is low and vice-versa
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