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Improving skin lesion segmentation in dermoscopic images by thin artefacts removal methods
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
2016 (English)Conference paper (Refereed)
Abstract [en]

In dermoscopic images, various thin artefacts naturally appear, most usually in the form of hairs. While trying to find the border of the skin lesion, these artefacts affect the lesion segmentation methods and also the subsequent classification.Currently, there is a lot of research focus in this area and various methods are presented both for skin lesion segmentation and thin artefacts removal. In this paper, we investigate into three different thin artefacts removal methods and compare their results using two different skin lesion segmentation methods. The segmentation results are compared with groundtruth segmentation. In addition, we introduce our novel artefacts removal method, which combined with the ExpectationMaximization image segmentation outperforms all the tested methods.

Place, publisher, year, edition, pages
2016.
Keyword [en]
thin artefacts removal, skin lesion seg- mentation, dermoscopic images, Chan-Vese segmentation, expectation-maximization segmentation
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:uu:diva-306892OAI: oai:DiVA.org:uu-306892DiVA: diva2:1044666
Conference
The 6th European Workshop on Visual Information Processing (EUVIP 2016)
Funder
Swedish Research Council, 621-2014-6153
Available from: 2016-11-04 Created: 2016-11-04 Last updated: 2016-11-04

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