Efficient and robust level set model for extracting regions of interest in X-ray welding images and MRI brain images

Chetih, Nabil; Boutiche, Yamina; Ramou, N.; Khorchef, Mohammed

doi:10.1007/s11042-023-15142-1

Cited by 1 publication

(2 citation statements)

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“…The best results of the VPC and VPE are obtained, when its optimal values are 1 and 0 respectively [4,40,41]. Tables 3-6 show the mean values of the VPC and the VPE for five methods: FCMC, KFCMC, NBWTFCM, FCMC-NBA and the proposed EIFCMNB method with noise standard deviations (σ) ranging from 5 to 20.…”

Section: Resultsmentioning

confidence: 99%

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Enhanced independent component analysis and fuzzy C-mean clustering based on novel bat algorithm for noisy image segmentation

Chetih,

Thelaidjia,

Boudani

2023

Eng. Res. Express

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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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Section: Resultsmentioning

confidence: 99%

“…better than the FCMC for the segmentation of noisy image. In order to quantify the performance improvement, we used criteria based on variance partition coefficient (VPC)[4,40,41] and variance partition entropy (VPE)[4,40,41]:…”

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confidence: 99%