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A soft kinetic data structure for lesion border detection

By Sinan Kockara, Mutlu Mete, Vincent Yip, Brendan Lee and Kemal Aydin


Motivation: The medical imaging and image processing techniques, ranging from microscopic to macroscopic, has become one of the main components of diagnostic procedures to assist dermatologists in their medical decision-making processes. Computer-aided segmentation and border detection on dermoscopic images is one of the core components of diagnostic procedures and therapeutic interventions for skin cancer. Automated assessment tools for dermoscopic images have become an important research field mainly because of inter- and intra-observer variations in human interpretations. In this study, a novel approach—graph spanner—for automatic border detection in dermoscopic images is proposed. In this approach, a proximity graph representation of dermoscopic images in order to detect regions and borders in skin lesion is presented

Topics: Ismb 2010 Conference Proceedings July 11 to July 13, 2010, Boston, Ma, Usa
Publisher: Oxford University Press
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Provided by: PubMed Central

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  1. (2007a) Unsupervised border detection in dermoscopy images.
  2. (2007b) A methodological approach to the classification of dermoscopy images.
  3. (2009a) Skin lesion extraction in dermoscopic images based on colour enhancement and iterative segmentation.
  4. (2009). (2009b) Skin lesion segmentation using cooperative neural network edge detection and colour normalisation.
  5. (1997). A software approach to hair removal from images.
  6. (2003). An Atlas of Surface Microscopy of Pigmented Skin Lesions: Dermoscopy.
  7. (2006). Comparison of color clustering algorithms for segmentation of dermatological images.
  8. (1999). Data structures for mobile data.
  9. (2006). Deformable spanners and applications.
  10. (2008). Dermoscopic hair disocclusion using inpainting.
  11. (2002). Dermoscopy: a Tutorial.
  12. (2007). Digital Image Processing: PIKS Inside.
  13. (2004). Efficient graph-based image segmentation.
  14. (1995). Epiluminescence microscopy. A useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists.
  15. (2005). Exploring protein folding trajectories using geometric spanners. Pacific Symp.
  16. (2003). Fast anisotropic gauss filtering.
  17. (2008). Feature-preserving artifact removal from dermoscopy images.
  18. (1998). Image and video segmentation: the normalized cut framework.
  19. (2007). Image processing, analysis, and machine vision. CengageEngineering, 2nd edn.
  20. (2007). Kinetic and dynamic data structures for convex hulls and upper envelopes.
  21. (1998). Kinetic data structures—a state of the art report. In Agarwal,P.K. et al. (eds)
  22. (2009). Lesion border detection in dermoscopy images.
  23. (2007). Median filtering in constant time.
  24. (1997). Normalized cuts and image segmentation.
  25. (2006). Quantitative assessment of tumor extraction from dermoscopy images and evaluation of computer-based extraction methods for automatic melanoma diagnostic system.
  26. (1998). Segmentation of dermatoscopic images by stabilized inverse diffusion equations.
  27. (1999). Segmentation of digitized dermatoscopic images bytwo-dimensional color clustering.
  28. (2001). Soft kinetic data structures.
  29. (1993). Statistical evaluation of epiluminescence dermoscopy criteria for melanocytic pigmented lesions.
  30. (1998). Techniques for a structural analysis of dermatoscopic imagery.