Edge enhancement for image segmentation using a RKHS method

Liam Burrows; Weihong Guo; Ke Chen; Francesco Torella

doi:10.1007/978-3-030-39343-4_17

Edge enhancement for image segmentation using a RKHS method

Liam Burrows, Weihong Guo, Ke Chen^*, Francesco Torella

^*Corresponding author for this work

Mathematics And Statistics

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

1 Citation (Scopus)

Abstract

Image segmentation has many important applications, particularly in medical imaging. Often medical images such as CTs have little contrast in them, and segmentation in such cases poses a great challenge to existing models without further user interaction. In this paper we propose an edge enhancement method based on the theory of reproducing kernel Hilbert spaces (RKHS) to model smooth components of an image, while separating the edges using approximated Heaviside functions. By modelling using this decomposition method, the approximated Heaviside function is capable of picking up more details than the usual method of using the image gradient. Further using this as an edge detector in a segmentation model can allow us to pick up a region of interest when low contrast between two objects is present and other models fail.

Original language	English
Title of host publication	Medical Image Understanding and Analysis - 23rd Conference, MIUA 2019, Proceedings
Editors	Yalin Zheng, Bryan M. Williams, Ke Chen
Place of Publication	Cham, Switzerland
Publisher	Springer
Pages	198-207
Number of pages	10
ISBN (Electronic)	9783030393434
ISBN (Print)	9783030393427
DOIs	https://doi.org/10.1007/978-3-030-39343-4_17
Publication status	Published - 24 Jan 2020
Event	23rd Conference on Medical Image Understanding and Analysis, MIUA 2019 - Liverpool, United Kingdom Duration: 24 Jul 2019 → 26 Jul 2019

Publication series

Name	Communications in Computer and Information Science
Volume	1065 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	23rd Conference on Medical Image Understanding and Analysis, MIUA 2019
Country/Territory	United Kingdom
City	Liverpool
Period	24/07/19 → 26/07/19

Funding

Work supported by UK EPSRC grant EP/N014499/1.

Keywords

heaviside function
image segmentation
RKHS

Access to Document

10.1007/978-3-030-39343-4_17

Cite this

Burrows, L., Guo, W., Chen, K., & Torella, F. (2020). Edge enhancement for image segmentation using a RKHS method. In Y. Zheng, B. M. Williams, & K. Chen (Eds.), Medical Image Understanding and Analysis - 23rd Conference, MIUA 2019, Proceedings (pp. 198-207). (Communications in Computer and Information Science; Vol. 1065 CCIS). Springer. https://doi.org/10.1007/978-3-030-39343-4_17

@inproceedings{c346d2f1e2b64bbd8502ed5692ae908a,

title = "Edge enhancement for image segmentation using a RKHS method",

abstract = "Image segmentation has many important applications, particularly in medical imaging. Often medical images such as CTs have little contrast in them, and segmentation in such cases poses a great challenge to existing models without further user interaction. In this paper we propose an edge enhancement method based on the theory of reproducing kernel Hilbert spaces (RKHS) to model smooth components of an image, while separating the edges using approximated Heaviside functions. By modelling using this decomposition method, the approximated Heaviside function is capable of picking up more details than the usual method of using the image gradient. Further using this as an edge detector in a segmentation model can allow us to pick up a region of interest when low contrast between two objects is present and other models fail.",

keywords = "heaviside function, image segmentation, RKHS",

author = "Liam Burrows and Weihong Guo and Ke Chen and Francesco Torella",

year = "2020",

month = jan,

day = "24",

doi = "10.1007/978-3-030-39343-4\_17",

language = "English",

isbn = "9783030393427",

series = "Communications in Computer and Information Science",

publisher = "Springer",

pages = "198--207",

editor = "Yalin Zheng and Williams, \{Bryan M.\} and Ke Chen",

booktitle = "Medical Image Understanding and Analysis - 23rd Conference, MIUA 2019, Proceedings",

note = "23rd Conference on Medical Image Understanding and Analysis, MIUA 2019 ; Conference date: 24-07-2019 Through 26-07-2019",

}

Burrows, L, Guo, W, Chen, K & Torella, F 2020, Edge enhancement for image segmentation using a RKHS method. in Y Zheng, BM Williams & K Chen (eds), Medical Image Understanding and Analysis - 23rd Conference, MIUA 2019, Proceedings. Communications in Computer and Information Science, vol. 1065 CCIS, Springer, Cham, Switzerland, pp. 198-207, 23rd Conference on Medical Image Understanding and Analysis, MIUA 2019, Liverpool, United Kingdom, 24/07/19. https://doi.org/10.1007/978-3-030-39343-4_17

Edge enhancement for image segmentation using a RKHS method. / Burrows, Liam; Guo, Weihong; Chen, Ke et al.
Medical Image Understanding and Analysis - 23rd Conference, MIUA 2019, Proceedings. ed. / Yalin Zheng; Bryan M. Williams; Ke Chen. Cham, Switzerland: Springer, 2020. p. 198-207 (Communications in Computer and Information Science; Vol. 1065 CCIS).

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

TY - GEN

T1 - Edge enhancement for image segmentation using a RKHS method

AU - Burrows, Liam

AU - Guo, Weihong

AU - Chen, Ke

AU - Torella, Francesco

PY - 2020/1/24

Y1 - 2020/1/24

N2 - Image segmentation has many important applications, particularly in medical imaging. Often medical images such as CTs have little contrast in them, and segmentation in such cases poses a great challenge to existing models without further user interaction. In this paper we propose an edge enhancement method based on the theory of reproducing kernel Hilbert spaces (RKHS) to model smooth components of an image, while separating the edges using approximated Heaviside functions. By modelling using this decomposition method, the approximated Heaviside function is capable of picking up more details than the usual method of using the image gradient. Further using this as an edge detector in a segmentation model can allow us to pick up a region of interest when low contrast between two objects is present and other models fail.

AB - Image segmentation has many important applications, particularly in medical imaging. Often medical images such as CTs have little contrast in them, and segmentation in such cases poses a great challenge to existing models without further user interaction. In this paper we propose an edge enhancement method based on the theory of reproducing kernel Hilbert spaces (RKHS) to model smooth components of an image, while separating the edges using approximated Heaviside functions. By modelling using this decomposition method, the approximated Heaviside function is capable of picking up more details than the usual method of using the image gradient. Further using this as an edge detector in a segmentation model can allow us to pick up a region of interest when low contrast between two objects is present and other models fail.

KW - heaviside function

KW - image segmentation

KW - RKHS

U2 - 10.1007/978-3-030-39343-4_17

DO - 10.1007/978-3-030-39343-4_17

M3 - Conference contribution book

AN - SCOPUS:85079102777

SN - 9783030393427

T3 - Communications in Computer and Information Science

SP - 198

EP - 207

BT - Medical Image Understanding and Analysis - 23rd Conference, MIUA 2019, Proceedings

A2 - Zheng, Yalin

A2 - Williams, Bryan M.

A2 - Chen, Ke

PB - Springer

CY - Cham, Switzerland

T2 - 23rd Conference on Medical Image Understanding and Analysis, MIUA 2019

Y2 - 24 July 2019 through 26 July 2019

ER -

Edge enhancement for image segmentation using a RKHS method

Abstract

Publication series

Conference

Funding

Keywords

Access to Document

Fingerprint

Cite this