Anisotropic mean shift based fuzzy C-means segmentation of dermoscopy images

Huiyu Zhou*, Gerald Schaefer, Abdul H. Sadka, M. Emre Celebi

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review


    Image segmentation is an important task in analysing dermoscopy images as the extraction of the borders of skin lesions provides important cues for accurate diagnosis. One family of segmentation algorithms is based on the idea of clustering pixels with similar characteristics. Fuzzy c-means has been shown to work well for clustering based segmentation, however due to its iterative nature this approach has excessive computational requirements. In this paper, we introduce a new mean shift based fuzzy c-means algorithm that requires less computational time than previous techniques while providing good segmentation results. 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 centers, the entire strategy is capable of effectively detecting regions within an image. Experimental results on a large dataset of diverse dermoscopy images demonstrate that the presented method accurately and efficiently detects the borders of skin lesions.

    Original languageEnglish
    Pages (from-to)26-34
    Number of pages9
    JournalIEEE Journal on Selected Topics in Signal Processing
    Issue number1
    Publication statusPublished - 20 Feb 2009


    • dermoscopy
    • fuzzy c-means
    • image segmentation
    • mean shift
    • melanoma
    • skin cancer


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