Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization, Mahalanobis distance and post-segmentation correction

Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images

“…Benaichouche et al [25] presented another improvement of the FCM clustering algorithm using particle swarm optimization (PSO) initialization, Mahalanobis distance and post segmentation correction. The first step introduced PSO initialization to overcome the trapping of the solution in local minima, the second step was concerned with the integration of the spatial grey level information and the Mahalanobis distance and the final step refined the segmentation results by reallocating the potentially misclassified pixels.…”

Image segmentation based on modified centroid weight particle swarm optimization and spatial fuzzy C-means clustering algorithm

“…N. Benaichouche, H. Oulhadj, and P. Siarry [12] have proposed an enhancement technique for image segmentation utilizing the fuzzy c-means clustering algorithm (FCM). The proposed technique has worked at three diverse stages.…”

Image quality assessment for contrast enhancement evaluation

“…At first, this algorithm was used to detect patterns of simultaneous birds' flight, sudden change of flight direction and optimal shape of their flocks. This algorithm has shown promising results in a variety of applications in image processing [12][13][14][15][16][17]. PSO algorithm has a good performance, simple implementation and fast convergence.…”