Literature DB >> 12423543

Validation of segmentation techniques for digital dermoscopy.

Guillod Joel1, Philippe Schmid-Saugeon, David Guggisberg, Jean Philippe Cerottini, Ralph Braun, Joakim Krischer, Jean-Hilaire Saurat, Kunt Murat.   

Abstract

PURPOSE: This study aims at evaluating two automatic contour detection techniques especially developed for dermoscopic images.
METHODS: Twenty-five images of lesions with a fuzzy boundary have been randomly selected. Five dermatologists experienced in dermoscopy have manually drawn the border of all the lesions and repeated the procedure after two and four weeks. The ability of a dermatologist to reproduce its own results was evaluated by measuring the non-overlapping area enclosed by its three successive contours. The interobserver variability evaluated the contour accuracy when using automatic or manual drawings. The mean probability that a pixel has been misclassified was computed for every observer and automatic technique.
RESULTS: Experts in dermoscopy are not able to reproduce measurements precisely and the two automatic techniques had a lower misclassification probability than those obtained by each dermatologist.
CONCLUSION: This study demonstrates that a single dermatologist should not be used as a reference, and subjective validation of lesion contour is inaccurate outside an experts's group. It is argued that image processing techniques for computer-aided diagnosis must show the best compromise within such a group.

Mesh:

Year:  2002        PMID: 12423543     DOI: 10.1034/j.1600-0846.2002.00334.x

Source DB:  PubMed          Journal:  Skin Res Technol        ISSN: 0909-752X            Impact factor:   2.365


  8 in total

1.  Unsupervised border detection in dermoscopy images.

Authors:  M Emre Celebi; Y Alp Aslandogan; William V Stoecker; Hitoshi Iyatomi; Hiroshi Oka; Xiaohe Chen
Journal:  Skin Res Technol       Date:  2007-11       Impact factor: 2.365

2.  A unified set of analysis tools for uterine cervix image segmentation.

Authors:  Zhiyun Xue; L Rodney Long; Sameer Antani; Leif Neve; Yaoyao Zhu; George R Thoma
Journal:  Comput Med Imaging Graph       Date:  2010-05-26       Impact factor: 4.790

3.  Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes.

Authors:  Bulent Erkol; Randy H Moss; R Joe Stanley; William V Stoecker; Erik Hvatum
Journal:  Skin Res Technol       Date:  2005-02       Impact factor: 2.365

4.  An improved objective evaluation measure for border detection in dermoscopy images.

Authors:  M Emre Celebi; Gerald Schaefer; Hitoshi Iyatomi; William V Stoecker; Joseph M Malters; James M Grichnik
Journal:  Skin Res Technol       Date:  2009-11       Impact factor: 2.365

Review 5.  Lesion border detection in dermoscopy images.

Authors:  M Emre Celebi; Hitoshi Iyatomi; Gerald Schaefer; William V Stoecker
Journal:  Comput Med Imaging Graph       Date:  2009-01-03       Impact factor: 4.790

6.  Automatic segmentation of dermoscopic images by iterative classification.

Authors:  Maciel Zortea; Stein Olav Skrøvseth; Thomas R Schopf; Herbert M Kirchesch; Fred Godtliebsen
Journal:  Int J Biomed Imaging       Date:  2011-07-17

7.  The feasibility of using manual segmentation in a multifeature computer-aided diagnosis system for classification of skin lesions: a retrospective comparative study.

Authors:  Wen-Yu Chang; Adam Huang; Yin-Chun Chen; Chi-Wei Lin; John Tsai; Chung-Kai Yang; Yin-Tseng Huang; Yi-Fan Wu; Gwo-Shing Chen
Journal:  BMJ Open       Date:  2015-05-03       Impact factor: 2.692

8.  Computer-aided diagnosis of skin lesions using conventional digital photography: a reliability and feasibility study.

Authors:  Wen-Yu Chang; Adam Huang; Chung-Yi Yang; Chien-Hung Lee; Yin-Chun Chen; Tian-Yau Wu; Gwo-Shing Chen
Journal:  PLoS One       Date:  2013-11-04       Impact factor: 3.240

  8 in total

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