Enhanced independent component analysis and fuzzy C-mean clustering based on novel bat algorithm for noisy image segmentation
Fuzzy c-means clustering is widely recognized as one of the most effective methods for image segmentation and achieving accurate classification. However, this method has two significant drawbacks: its sensitivity to noise and its convergence to local minimum clusters’ centroids. In this paper, we pr...
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| Published in: | Engineering Research Express Vol. 5; no. 4; pp. 45041 - 45058 |
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01.12.2023
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| Abstract | Fuzzy c-means clustering is widely recognized as one of the most effective methods for image segmentation and achieving accurate classification. However, this method has two significant drawbacks: its sensitivity to noise and its convergence to local minimum clusters’ centroids. In this paper, we proposed a novel model called EIFCMNB, which incorporates enhanced independent component analysis (EICA), fuzzy c-means clustering (FCMC) and novel bat algorithm (NBA) for noise image segmentation. The suggested model consists of two main phases: image denoising and extraction of the regions of interest (ROIs). In the first phase, the enhanced independent component analysis (EICA) algorithm is used for recovering a good quality image, from a noisy image of poor quality. Several noisy images, with noise variances ranging from 5 to 20, were filtered. The resulting images were then evaluated based on several criteria viz: Peak Signal to Noise Ratio (PSNR), Relative Norm Error (RE), Normalized Cross-Correlation (NCC), and Structural Similarity index measure (SSIM). In the second phase, the fuzzy c-means clustering based on a novel bat algorithm is adopted to calculate optimal clusters’ centroids and extract the ROIs. By incorporating the new bat algorithm, we aim to overcome the problem of converging to local minimums and achieve improved segmentation accuracy. Promising experimental results have been obtained by applying the proposed model to MRI brain images and x-ray welding images. Two criteria viz: VPE end VPC have been employed to evaluate the suggested approach. The experiments clearly demonstrate that our suggested model effectively addresses the sensitivity to noise problem and provides optimal clusters’ centroids. Moreover, it outperforms several FCMC-based algorithms, exhibiting superior performance in terms of image segmentation and classification. |
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| AbstractList | Fuzzy c-means clustering is widely recognized as one of the most effective methods for image segmentation and achieving accurate classification. However, this method has two significant drawbacks: its sensitivity to noise and its convergence to local minimum clusters’ centroids. In this paper, we proposed a novel model called EIFCMNB, which incorporates enhanced independent component analysis (EICA), fuzzy c-means clustering (FCMC) and novel bat algorithm (NBA) for noise image segmentation. The suggested model consists of two main phases: image denoising and extraction of the regions of interest (ROIs). In the first phase, the enhanced independent component analysis (EICA) algorithm is used for recovering a good quality image, from a noisy image of poor quality. Several noisy images, with noise variances ranging from 5 to 20, were filtered. The resulting images were then evaluated based on several criteria viz: Peak Signal to Noise Ratio (PSNR), Relative Norm Error (RE), Normalized Cross-Correlation (NCC), and Structural Similarity index measure (SSIM). In the second phase, the fuzzy c-means clustering based on a novel bat algorithm is adopted to calculate optimal clusters’ centroids and extract the ROIs. By incorporating the new bat algorithm, we aim to overcome the problem of converging to local minimums and achieve improved segmentation accuracy. Promising experimental results have been obtained by applying the proposed model to MRI brain images and x-ray welding images. Two criteria viz: VPE end VPC have been employed to evaluate the suggested approach. The experiments clearly demonstrate that our suggested model effectively addresses the sensitivity to noise problem and provides optimal clusters’ centroids. Moreover, it outperforms several FCMC-based algorithms, exhibiting superior performance in terms of image segmentation and classification. |
| Author | Chetih, Nabil Boudani, Fatma Zohra Thelaidjia, Tawfik |
| Author_xml | – sequence: 1 givenname: Nabil orcidid: 0009-0008-9706-003X surname: Chetih fullname: Chetih, Nabil organization: Research Center in Industrial Technologies CRTI, PO Box 64, Cheraga 16014, Algiers, Algeria – sequence: 2 givenname: Tawfik surname: Thelaidjia fullname: Thelaidjia, Tawfik organization: Larbi Tebessi University LABGET Laboratory, Department of Electrical Engineering, Faculty of Science and Technology, Tebessa, Algeria – sequence: 3 givenname: Fatma Zohra surname: Boudani fullname: Boudani, Fatma Zohra organization: Faculty of Electrical Engineering, USTHB, BP 32 El-Alia, 16111 Bab Ezzouar, Algiers, Algeria |
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| SubjectTerms | enhanced independent component analysis fuzzy c-means clustering image segmentation noisy images novel bat algorithm regions of interest |
| Title | Enhanced independent component analysis and fuzzy C-mean clustering based on novel bat algorithm for noisy image segmentation |
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