Canny Edge Detection Enhancement by Scale Multiplication
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation Based on Adaptive Cluster Prototype Estimation
IEEE Transactions on Fuzzy Systems
Adaptive perceptual color-texture image segmentation
IEEE Transactions on Image Processing
A morphological gradient approach to color edge detection
IEEE Transactions on Image Processing
A Class-Adaptive Spatially Variant Mixture Model for Image Segmentation
IEEE Transactions on Image Processing
Segmentation by Fusion of Histogram-Based -Means Clusters in Different Color Spaces
IEEE Transactions on Image Processing
Cytoplasm image segmentation by spatial fuzzy clustering
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
Monte Carlo cluster refinement for noise robust image segmentation
Journal of Visual Communication and Image Representation
LS-SVM based image segmentation using color and texture information
Journal of Visual Communication and Image Representation
Color texture segmentation based on image pixel classification
Engineering Applications of Artificial Intelligence
Kernel generalized fuzzy c-means clustering with spatial information for image segmentation
Digital Signal Processing
Effective FCM noise clustering algorithms in medical images
Computers in Biology and Medicine
Pattern Recognition Letters
A modified interval type-2 fuzzy C-means algorithm with application in MR image segmentation
Pattern Recognition Letters
Modular interpretation of low altitude aerial images of non-urban environment
Digital Signal Processing
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Automated segmentation of images has been considered an important intermediate processing task to extract semantic meaning from pixels. In general, the fuzzy c-means approach (FCM) is highly effective for image segmentation. But for the conventional FCM image segmentation algorithm, cluster assignment is based solely on the distribution of pixel attributes in the feature space, and the spatial distribution of pixels in an image is not taken into consideration. In this paper, we present a novel FCM image segmentation scheme by utilizing local contextual information and the high inter-pixel correlation inherent. Firstly, a local spatial similarity measure model is established, and the initial clustering center and initial membership are determined adaptively based on local spatial similarity measure model. Secondly, the fuzzy membership function is modified according to the high inter-pixel correlation inherent. Finally, the image is segmented by using the modified FCM algorithm. Experimental results showed the proposed method achieves competitive segmentation results compared to other FCM-based methods, and is in general faster.