Fuzzy Sets and Systems
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
On efficiency of optimization in fuzzy c-means
Neural, Parallel & Scientific Computations
Mean Shift Analysis and Applications
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Fully automated biomedical image segmentation by self-organized model adaptation
Neural Networks - 2004 Special issue: New developments in self-organizing systems
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Image segmentation is a crucial stage in the analysis of dermoscopic images as the extraction of exact boundaries of skin lesions is esseintial for accurate diagnosis. One approach to image segmentation is based on the idea of clustering pixels with similar characteristics. Fuzzy c-means is a popular clustering based algorithm that is often employed in medical image segmentation, however due to its iterative nature also has excessive computational requirements. In this paper we introduce a new mean shift based fuzzy c-means algorithm that requires less computational time compared to previous techniques while providing good segmentation performance. The proposed segmentation method incorporates a mean field term within the standard fuzzy c-means objective function. Since mean shift can quickly and reliably find cluster centres, the entire strategy is capable of effeciently detecting regions within an image. Experimental results on a large dataset of dermoscopic images demonstrates that our algorithm is able to accurately and efficiently extract skin lesion borders.