Incorporating Feature Based Priors into
the Geodesic Active Contour Model and its
Application in Biomedical Imagery

Huaizhong Zhang; Philip Morrow; Sally Mclean; Kurt Saetzler

doi:10.1109/IMVIP.2007.23

Incorporating Feature Based Priors into the Geodesic Active Contour Model and its Application in Biomedical Imagery

Huaizhong Zhang, Philip Morrow, Sally Mclean, Kurt Saetzler

Computer Science

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

3 Citations (Scopus)

Abstract

This paper presents improvements to the geodesic active contour (GAC) model obtained by incorporating user defined prior information into the model itself. Specifically, the stopping function in the GAC model is revised by designing an indicator function derived from a-priori information. The numerical implementation is based on the level set technique. Experimental results illustrate that our approach is efficient and feasible for both artificial and real images. In particular, the proposed method performs well in situations where existing methods are known to fail.

Original language	English
Title of host publication	Not Known
Pages	67-74
DOIs	https://doi.org/10.1109/IMVIP.2007.23
Publication status	E-pub ahead of print - 24 Sept 2007
Event	International Machine Vision and Image Processing Conference 2007 (IMVIP 2007) - National University of Ireland, Maynooth, Ireland Duration: 5 Sept 2007 → 7 Sept 2007

Conference

Conference	International Machine Vision and Image Processing Conference 2007 (IMVIP 2007)
Country/Territory	Ireland
City	Maynooth
Period	5/09/07 → 7/09/07

Access to Document

10.1109/IMVIP.2007.23

http://ieeexplore.ieee.org/abstract/document/4318139/

Cite this

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title = "Incorporating Feature Based Priors into the Geodesic Active Contour Model and its Application in Biomedical Imagery",

abstract = "This paper presents improvements to the geodesic active contour (GAC) model obtained by incorporating user defined prior information into the model itself. Specifically, the stopping function in the GAC model is revised by designing an indicator function derived from a-priori information. The numerical implementation is based on the level set technique. Experimental results illustrate that our approach is efficient and feasible for both artificial and real images. In particular, the proposed method performs well in situations where existing methods are known to fail.",

author = "Huaizhong Zhang and Philip Morrow and Sally Mclean and Kurt Saetzler",

year = "2007",

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doi = "10.1109/IMVIP.2007.23",

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note = "International Machine Vision and Image Processing Conference 2007 (IMVIP 2007) ; Conference date: 05-09-2007 Through 07-09-2007",

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T1 - Incorporating Feature Based Priors into the Geodesic Active Contour Model and its Application in Biomedical Imagery

AU - Zhang, Huaizhong

AU - Morrow, Philip

AU - Mclean, Sally

AU - Saetzler, Kurt

PY - 2007/9/24

Y1 - 2007/9/24

N2 - This paper presents improvements to the geodesic active contour (GAC) model obtained by incorporating user defined prior information into the model itself. Specifically, the stopping function in the GAC model is revised by designing an indicator function derived from a-priori information. The numerical implementation is based on the level set technique. Experimental results illustrate that our approach is efficient and feasible for both artificial and real images. In particular, the proposed method performs well in situations where existing methods are known to fail.

AB - This paper presents improvements to the geodesic active contour (GAC) model obtained by incorporating user defined prior information into the model itself. Specifically, the stopping function in the GAC model is revised by designing an indicator function derived from a-priori information. The numerical implementation is based on the level set technique. Experimental results illustrate that our approach is efficient and feasible for both artificial and real images. In particular, the proposed method performs well in situations where existing methods are known to fail.

U2 - 10.1109/IMVIP.2007.23

DO - 10.1109/IMVIP.2007.23

M3 - Conference proceeding (ISBN)

SP - 67

EP - 74

BT - Not Known

T2 - International Machine Vision and Image Processing Conference 2007 (IMVIP 2007)

Y2 - 5 September 2007 through 7 September 2007

ER -

Incorporating Feature Based Priors into the Geodesic Active Contour Model and its Application in Biomedical Imagery

Abstract

Conference

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