A curvelet-based morphological segmentation of abdominal CT images

M. Sakalli; T. D. Pham; Kin Man Lam; H. Yan

doi:10.1109/EMBC.2014.6944882

A curvelet-based morphological segmentation of abdominal CT images

M. Sakalli, T. D. Pham, Kin Man Lam, H. Yan

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

2 Citations (Scopus)

Abstract

This paper presents a segmentation methodology of abdominal axial CT images. The aim of the study is to determine the location of mesenteric area from the axial images so the organs enclosed within can be localized precisely for diagnostic purposes. The challenge confronted here is that there is no a certain deterministic shape of abdominal organs. The methodology implemented here utilizes a curvelets stage followed by morphological image processing to achieve a contour emphasized segmentation from the gestalts of surrounding organs. This paper gives a detailed analysis of approach taken with the problems faced and a brief comparison wrt to other wavelet approaches.

Original language	English
Title of host publication	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
Publisher	IEEE
Pages	5542-5545
Number of pages	4
ISBN (Electronic)	9781424479290
DOIs	https://doi.org/10.1109/EMBC.2014.6944882
Publication status	Published - 1 Jan 2014
Event	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States Duration: 26 Aug 2014 → 30 Aug 2014

Conference

Conference	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
Country/Territory	United States
City	Chicago
Period	26/08/14 → 30/08/14

Keywords

abdominal image segmentation
connected-components labeling
curvelets
edge and contour detection
non-maximal suppression
wavelets

ASJC Scopus subject areas

Health Informatics
Computer Science Applications
Biomedical Engineering
General Medicine

More information

10.1109/EMBC.2014.6944882

Cite this

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title = "A curvelet-based morphological segmentation of abdominal CT images",

abstract = "This paper presents a segmentation methodology of abdominal axial CT images. The aim of the study is to determine the location of mesenteric area from the axial images so the organs enclosed within can be localized precisely for diagnostic purposes. The challenge confronted here is that there is no a certain deterministic shape of abdominal organs. The methodology implemented here utilizes a curvelets stage followed by morphological image processing to achieve a contour emphasized segmentation from the gestalts of surrounding organs. This paper gives a detailed analysis of approach taken with the problems faced and a brief comparison wrt to other wavelet approaches.",

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Sakalli, M, Pham, TD, Lam, KM & Yan, H 2014, A curvelet-based morphological segmentation of abdominal CT images. in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014., 6944882, IEEE, pp. 5542-5545, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, Chicago, United States, 26/08/14. https://doi.org/10.1109/EMBC.2014.6944882

A curvelet-based morphological segmentation of abdominal CT images. / Sakalli, M.; Pham, T. D.; Lam, Kin Man et al.
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. IEEE, 2014. p. 5542-5545 6944882.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - This paper presents a segmentation methodology of abdominal axial CT images. The aim of the study is to determine the location of mesenteric area from the axial images so the organs enclosed within can be localized precisely for diagnostic purposes. The challenge confronted here is that there is no a certain deterministic shape of abdominal organs. The methodology implemented here utilizes a curvelets stage followed by morphological image processing to achieve a contour emphasized segmentation from the gestalts of surrounding organs. This paper gives a detailed analysis of approach taken with the problems faced and a brief comparison wrt to other wavelet approaches.

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KW - connected-components labeling

KW - curvelets

KW - edge and contour detection

KW - non-maximal suppression

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ER -

A curvelet-based morphological segmentation of abdominal CT images

Abstract

Conference

Keywords

ASJC Scopus subject areas

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