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In order to solve the problem of noise sensitivity and low precision in image segmentation, a new image segmentation method based on super-pixel with neighborhood constrained and multi-level FCM clustering is proposed. Firstly, an improved SLIC algorithm of FCM is designed based on the local pixel region of the image to get the refined super-pixel. Then, the improved FCM clustering algorithm is combined with the spatial relationship of neighborhood super-pixel to mark the super-pixel categories. In order to verify the effectiveness of the algorithm, the artificial synthetic image with noise and natural image without noise are used for comparison experiments. The experimental result show the algorithm proposed in this paper can not only suppress the noise, but also improve the processing accuracy. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the existing image segmentation algorithm is complex and the segmentation precision is not high enough, this paper proposed a medical image clustering segmentation algorithm based on geometric distance optimization centroid and rough fuzzy C-means ( RFCM). First it set up the set of pixels represented by the soft set and calculated the distance between each pixel and the centroid. Then it grouped the pixels into clusters based on the minimum distance between the pixel and the centroid. In order to apply the soft set to the coarse fuzzy C-means, it defined a fuzzy soft set. It further converted the input image into a binary image, and selected an appropriate centroid by calculating the geometric distance of the connected region. Finally, using these new centroids to calculate the membership value of the updated pixel, it completed the fuzzy clustering. The performance of the proposed hybrid algorithm was evaluated on three medical databases, such as Allen Brain Atlas. The obtained Jaccard coefficient and segmentation accuracy (SA) were better than several comparison algorithms. Experiments show that the proposed clustering segmentation algorithm has good performance. [ABSTRACT FROM AUTHOR]

Image segmentation process is one of the most interesting and challenging problems in digital image processing tasks. The segmentation process involves finding similar regions within an image. Many segmentation problems are achieved by the incorporation of clustering techniques. One of the most common technique for clustering process is the Fuzzy C-means (FCM) algorithm. However, even when FCM is one of the most popular techniques applied in image segmentation, it presents some issues such as large computational time complexity, noise sensitivity, and initial cluster centers dependency. In order to solve these problems, this paper presents a Histogram Based Fuzzy Clustering (HBFC) technique using an improved version of Firefly Algorithm (FA). In the proposed approach, the FA involves three search strategies: rough set-based population, random attraction and local search mechanism. Also, the clustering process is conducted based on gray level histograms instead of single pixels of an image. Under such circumstances, the occurrence of misclassification of pixels is reduced. A rigorous comparative study is conducted among the proposed approach and several state-of-art Nature-Inspired Optimization Algorithms (NIOAs) and traditional clustering techniques. The numerical results indicate that the proposed approach outperform the well-known NIOA based clustering methods in terms of precision, robustness and quality of the segmented outputs. • An improved Firefly algorithm has been proposed for Image Clustering. • Random Attraction model and rough set have been utilized. • The proposed approach is a histogram based fast fuzzy clustering model. • The proposed method is compared against state-of-art segmentation methods. • Numerical results indicate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]