Depth Data Improves Skin Lesion Segmentation

Xiang Li; Benjamin Aldridge; Lucia Ballerini; Robert Fisher; Jonathan Rees

doi:10.1007/978-3-642-04271-3_133

Depth Data Improves Skin Lesion Segmentation

Xiang Li, Benjamin Aldridge, Lucia Ballerini, Robert Fisher, Jonathan Rees

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

This paper shows that adding 3D depth information to RGB colour images improves segmentation of pigmented and non-pigmented skin lesion. A region-based active contour segmentation approach using a statistical model based on the level-set framework is presented. We consider what kinds of properties (e.g., colour, depth, texture) are most discriminative. The experiments show that our proposed method integrating chromatic and geometric information produces segmentation results for pigmented lesions close to dermatologists and more consistent and accurate results for non-pigmented lesions.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2009
Subtitle of host publication	12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part II
Editors	Guang-Zhong Yang, David Hawkes, Daniel Rueckert, Alison Noble, Chris Taylor
Publisher	Springer
Pages	1100-1107
Number of pages	8
ISBN (Print)	978-3-642-04270-6
DOIs	https://doi.org/10.1007/978-3-642-04271-3_133
Publication status	Published - 2009

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Berlin / Heidelberg
Volume	5762
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Access to Document

10.1007/978-3-642-04271-3_133

Li Aldridge et al 2009 Depth Data Improves Skin Lesion SegmentationSubmitted manuscript, 3.02 MB

http://link.springer.com/chapter/10.1007%2F978-3-642-04271-3_133

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Li, X., Aldridge, B., Ballerini, L., Fisher, R., & Rees, J. (2009). Depth Data Improves Skin Lesion Segmentation. In G.-Z. Yang, D. Hawkes, D. Rueckert, A. Noble, & C. Taylor (Eds.), Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2009: 12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part II (pp. 1100-1107). (Lecture Notes in Computer Science; Vol. 5762). Springer. https://doi.org/10.1007/978-3-642-04271-3_133

Li, Xiang ; Aldridge, Benjamin ; Ballerini, Lucia et al. / Depth Data Improves Skin Lesion Segmentation. Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2009: 12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part II. editor / Guang-Zhong Yang ; David Hawkes ; Daniel Rueckert ; Alison Noble ; Chris Taylor. Springer, 2009. pp. 1100-1107 (Lecture Notes in Computer Science).

@inproceedings{5a659429257f49b6ab5daa9d13f3942a,

title = "Depth Data Improves Skin Lesion Segmentation",

abstract = "This paper shows that adding 3D depth information to RGB colour images improves segmentation of pigmented and non-pigmented skin lesion. A region-based active contour segmentation approach using a statistical model based on the level-set framework is presented. We consider what kinds of properties (e.g., colour, depth, texture) are most discriminative. The experiments show that our proposed method integrating chromatic and geometric information produces segmentation results for pigmented lesions close to dermatologists and more consistent and accurate results for non-pigmented lesions. ",

author = "Xiang Li and Benjamin Aldridge and Lucia Ballerini and Robert Fisher and Jonathan Rees",

year = "2009",

doi = "10.1007/978-3-642-04271-3_133",

language = "English",

isbn = "978-3-642-04270-6",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "1100--1107",

editor = "Guang-Zhong Yang and David Hawkes and Daniel Rueckert and Alison Noble and Chris Taylor",

booktitle = "Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2009",

address = "United Kingdom",

}

Li, X, Aldridge, B, Ballerini, L, Fisher, R & Rees, J 2009, Depth Data Improves Skin Lesion Segmentation. in G-Z Yang, D Hawkes, D Rueckert, A Noble & C Taylor (eds), Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2009: 12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part II. Lecture Notes in Computer Science, vol. 5762, Springer, pp. 1100-1107. https://doi.org/10.1007/978-3-642-04271-3_133

Depth Data Improves Skin Lesion Segmentation. / Li, Xiang; Aldridge, Benjamin; Ballerini, Lucia et al.
Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2009: 12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part II. ed. / Guang-Zhong Yang; David Hawkes; Daniel Rueckert; Alison Noble; Chris Taylor. Springer, 2009. p. 1100-1107 (Lecture Notes in Computer Science; Vol. 5762).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Depth Data Improves Skin Lesion Segmentation

AU - Li, Xiang

AU - Aldridge, Benjamin

AU - Ballerini, Lucia

AU - Fisher, Robert

AU - Rees, Jonathan

PY - 2009

Y1 - 2009

N2 - This paper shows that adding 3D depth information to RGB colour images improves segmentation of pigmented and non-pigmented skin lesion. A region-based active contour segmentation approach using a statistical model based on the level-set framework is presented. We consider what kinds of properties (e.g., colour, depth, texture) are most discriminative. The experiments show that our proposed method integrating chromatic and geometric information produces segmentation results for pigmented lesions close to dermatologists and more consistent and accurate results for non-pigmented lesions.

AB - This paper shows that adding 3D depth information to RGB colour images improves segmentation of pigmented and non-pigmented skin lesion. A region-based active contour segmentation approach using a statistical model based on the level-set framework is presented. We consider what kinds of properties (e.g., colour, depth, texture) are most discriminative. The experiments show that our proposed method integrating chromatic and geometric information produces segmentation results for pigmented lesions close to dermatologists and more consistent and accurate results for non-pigmented lesions.

U2 - 10.1007/978-3-642-04271-3_133

DO - 10.1007/978-3-642-04271-3_133

M3 - Conference contribution

SN - 978-3-642-04270-6

T3 - Lecture Notes in Computer Science

SP - 1100

EP - 1107

BT - Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2009

A2 - Yang, Guang-Zhong

A2 - Hawkes, David

A2 - Rueckert, Daniel

A2 - Noble, Alison

A2 - Taylor, Chris

PB - Springer

ER -

Li X, Aldridge B, Ballerini L, Fisher R, Rees J. Depth Data Improves Skin Lesion Segmentation. In Yang GZ, Hawkes D, Rueckert D, Noble A, Taylor C, editors, Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2009: 12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part II. Springer. 2009. p. 1100-1107. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-04271-3_133

Depth Data Improves Skin Lesion Segmentation

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