Snake-Aided Automatic Organ Delineation

Weibing Xu; Saad A. Amin; Olivier C.L. Haas; Keith J. Burnham; John A. Mills

doi:10.1007/978-3-540-28649-3_62

Snake-Aided Automatic Organ Delineation

Weibing Xu, Saad A. Amin, Olivier C.L. Haas, Keith J. Burnham, John A. Mills

Coventry University

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Abstract

This paper presents a knowledge-based image segmentation tool for organ delineation in CT (Computed Tomography) images. The noise and low contrast make the detection difficult. Therefore in this method, radial search, noise reduction method and post-processing algorithm have been implemented to improve the quality of contour detection. Three edge detection algorithms have been used and after detection several optimization methods have been employed to get the accurate contour from three detected contours. Finally to achieve higher accuracy of detection, active contour model (ACM), snake, has been used after the contour detected by previous methods.

Original language	English
Title of host publication	Pattern Recognition: 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004. Proceedings
Editors	Carl Edward Rasmussen, Heinrich H. Bülthoff, Bernhard Schölkopf, Martin A. Giese
Publisher	Springer Verlag
Pages	504-511
Number of pages	8
Volume	Lecture Notes in Computer Science 3175
ISBN (Print)	978-3-540-22945-2
DOIs	https://doi.org/10.1007/978-3-540-28649-3_62
Publication status	Published - 2004
Event	DAGM Symposium 2004 - Tubingen, Germany Duration: 30 Aug 2004 → 1 Sept 2004

Conference

Conference	DAGM Symposium 2004
Country/Territory	Germany
City	Tubingen
Period	30/08/04 → 1/09/04

Bibliographical note

This article is not yet available on the repository. The paper was given at the 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004.

Keywords

Pattern Recognition
Image Processing and Computer Vision
Artificial Intelligence (incl. Robotics)
Computer Graphics
Algorithm Analysis and Problem Complexity

Access to Document

10.1007/978-3-540-28649-3_62

Cite this

Xu, W., Amin, S. A., Haas, O. C. L., Burnham, K. J., & Mills, J. A. (2004). Snake-Aided Automatic Organ Delineation. In C. E. Rasmussen, H. H. Bülthoff, B. Schölkopf, & M. A. Giese (Eds.), Pattern Recognition: 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004. Proceedings (Vol. Lecture Notes in Computer Science 3175, pp. 504-511). Springer Verlag. https://doi.org/10.1007/978-3-540-28649-3_62

Snake-Aided Automatic Organ Delineation. / Xu, Weibing; Amin, Saad A.; Haas, Olivier C.L. et al.

Pattern Recognition: 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004. Proceedings. ed. / Carl Edward Rasmussen; Heinrich H. Bülthoff; Bernhard Schölkopf; Martin A. Giese. Vol. Lecture Notes in Computer Science 3175 Springer Verlag, 2004. p. 504-511.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Xu, W, Amin, SA, Haas, OCL, Burnham, KJ & Mills, JA 2004, Snake-Aided Automatic Organ Delineation. in CE Rasmussen, HH Bülthoff, B Schölkopf & MA Giese (eds), Pattern Recognition: 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004. Proceedings. vol. Lecture Notes in Computer Science 3175, Springer Verlag, pp. 504-511, DAGM Symposium 2004, Tubingen, Germany, 30/08/04. https://doi.org/10.1007/978-3-540-28649-3_62

Xu W, Amin SA, Haas OCL, Burnham KJ, Mills JA. Snake-Aided Automatic Organ Delineation. In Rasmussen CE, Bülthoff HH, Schölkopf B, Giese MA, editors, Pattern Recognition: 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004. Proceedings. Vol. Lecture Notes in Computer Science 3175. Springer Verlag. 2004. p. 504-511 doi: 10.1007/978-3-540-28649-3_62

Xu, Weibing ; Amin, Saad A. ; Haas, Olivier C.L. et al. / Snake-Aided Automatic Organ Delineation. Pattern Recognition: 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004. Proceedings. editor / Carl Edward Rasmussen ; Heinrich H. Bülthoff ; Bernhard Schölkopf ; Martin A. Giese. Vol. Lecture Notes in Computer Science 3175 Springer Verlag, 2004. pp. 504-511

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abstract = "This paper presents a knowledge-based image segmentation tool for organ delineation in CT (Computed Tomography) images. The noise and low contrast make the detection difficult. Therefore in this method, radial search, noise reduction method and post-processing algorithm have been implemented to improve the quality of contour detection. Three edge detection algorithms have been used and after detection several optimization methods have been employed to get the accurate contour from three detected contours. Finally to achieve higher accuracy of detection, active contour model (ACM), snake, has been used after the contour detected by previous methods.",

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author = "Weibing Xu and Amin, {Saad A.} and Haas, {Olivier C.L.} and Burnham, {Keith J.} and Mills, {John A.}",

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N1 - This article is not yet available on the repository. The paper was given at the 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004.

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N2 - This paper presents a knowledge-based image segmentation tool for organ delineation in CT (Computed Tomography) images. The noise and low contrast make the detection difficult. Therefore in this method, radial search, noise reduction method and post-processing algorithm have been implemented to improve the quality of contour detection. Three edge detection algorithms have been used and after detection several optimization methods have been employed to get the accurate contour from three detected contours. Finally to achieve higher accuracy of detection, active contour model (ACM), snake, has been used after the contour detected by previous methods.

AB - This paper presents a knowledge-based image segmentation tool for organ delineation in CT (Computed Tomography) images. The noise and low contrast make the detection difficult. Therefore in this method, radial search, noise reduction method and post-processing algorithm have been implemented to improve the quality of contour detection. Three edge detection algorithms have been used and after detection several optimization methods have been employed to get the accurate contour from three detected contours. Finally to achieve higher accuracy of detection, active contour model (ACM), snake, has been used after the contour detected by previous methods.

KW - Pattern Recognition

KW - Image Processing and Computer Vision

KW - Artificial Intelligence (incl. Robotics)

KW - Computer Graphics

KW - Algorithm Analysis and Problem Complexity

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SP - 504

EP - 511

BT - Pattern Recognition: 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004. Proceedings

A2 - Rasmussen, Carl Edward

A2 - Bülthoff, Heinrich H.

A2 - Schölkopf, Bernhard

A2 - Giese, Martin A.

PB - Springer Verlag

T2 - DAGM Symposium 2004

Y2 - 30 August 2004 through 1 September 2004

ER -

Snake-Aided Automatic Organ Delineation

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Keywords

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