Abstract
The spatial constrained Fuzzy C-means clustering (FCM) is an effective algorithm for image segmentation. Its background information improves the insensitivity to noise to some extent. In addition, the membership degree of Euclidean distance is not suitable for revealing the non-Euclidean structure of input data, since it still lacks enough robustness to noise and outliers. In order to overcome the problem above, this paper proposes a new kernel-based algorithm based on the Kernel-induced Distance Measure, which we call it Kernel-based Robust Bias-correction Fuzzy Weighted C-ordered-means Clustering Algorithm (KBFWCM). In the construction of the objective function, KBFWCM algorithm comprehensively takes into account that the spatial constrained FCM clustering algorithm is insensitive to image noise and involves a highly intensive computation. Aiming at the insensitivity of spatial constrained FCM clustering algorithm to noise and its image detail processing, the KBFWCM algorithm proposes a comprehensive algorithm combining fuzzy local similarity measures (space and grayscale) and the typicality of data attributes. Aiming at the poor robustness of the original algorithm to noise and outliers and its highly intensive computation, a Kernel-based clustering method that includes a class of robust non-Euclidean distance measures is proposed in this paper. The experimental results show that the KBFWCM algorithm has a stronger denoising and robust effect on noise image.
Highlights
Clustering analysis is a significant technique in data analysis, which covers a wide range of applications in many areas such as data mining [1,2], image processing [3,4,5], computer vision [6]and artificial intelligence [7,8]
Outliers and noisy data items are classified as low typicality without damaging the clustering process; (3) In order to overcome the problem that the distance between one point and two prototypes is equal in the fuzzy c-means (FCM), Krishnapuram and Keller [43,44] proposed a new clustering model called Possibilistic C-Means (PCM)
The intensity inhomogeneity caused by radiofrequency (RF) coils in magnetic resonance imaging (MRI) is because this algorithm only takes into account the grayscale information without considering the spatial information and because it cannot avoid the noise interference
Summary
Clustering analysis is a significant technique in data analysis, which covers a wide range of applications in many areas such as data mining [1,2], image processing [3,4,5], computer vision [6]. Image Segmentation is a image processing technique, dividing the image into several specific non-overlapping regions with unique characteristics and proposing objectives of interest. The existing segmentation methods mainly consist of the following categories: threshold-based segmentation, region-based segmentation, edge-based segmentation, specific theory-based segmentation and so forth. Speaking, these methods contain clustering [10,11], region growing [12], watershed transformation [13], active contour model [14], meanShift [15], graph Cut [16], spectral clustering [17], Symmetry 2019, 11, 753; doi:10.3390/sym11060753 www.mdpi.com/journal/symmetry. There have been many types of methods for image segmentation [22,23,24,25], these methods are all not robust and effective enough for a large number of different images
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