Mean shape models for polyp detection in ct colonography

Ju Lynn Ong; Abd Krim Seghouane; Kevin Osborn

doi:10.1109/DICTA.2008.9

Mean shape models for polyp detection in ct colonography

Ju Lynn Ong, Abd Krim Seghouane, Kevin Osborn

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

4 Citations (Scopus)

Abstract

A global representation of polyp and non polyp shapes are constructed following a point distribution model (PDM) as an alternative to current methods which only inspect local shape characteristics at a point on the surface. The decision on whether or not a candidate lesion is a polyp can then be made by comparing the minimum Euclidean distance of the candidate lesion to the constructed mean shapes. The model closer in distance to the candidate lesion is selected to represent that particular lesion - polyp or non polyp. This shape model can also be used to investigate the shape variability of the different lesions detected by constructing an allowable shape domain for each of these lesions.

Original language	English
Title of host publication	Proceedings - Digital Image Computing
Subtitle of host publication	Techniques and Applications, DICTA 2008
Pages	287-293
Number of pages	7
DOIs	https://doi.org/10.1109/DICTA.2008.9
Publication status	Published - 2008
Event	Digital Image Computing: Techniques and Applications, DICTA 2008 - Canberra, ACT, Australia Duration: 1 Dec 2008 → 3 Dec 2008

Publication series

Name	Proceedings - Digital Image Computing: Techniques and Applications, DICTA 2008

Conference

Conference	Digital Image Computing: Techniques and Applications, DICTA 2008
Country/Territory	Australia
City	Canberra, ACT
Period	1/12/08 → 3/12/08

Access to Document

10.1109/DICTA.2008.9

Cite this

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title = "Mean shape models for polyp detection in ct colonography",

abstract = "A global representation of polyp and non polyp shapes are constructed following a point distribution model (PDM) as an alternative to current methods which only inspect local shape characteristics at a point on the surface. The decision on whether or not a candidate lesion is a polyp can then be made by comparing the minimum Euclidean distance of the candidate lesion to the constructed mean shapes. The model closer in distance to the candidate lesion is selected to represent that particular lesion - polyp or non polyp. This shape model can also be used to investigate the shape variability of the different lesions detected by constructing an allowable shape domain for each of these lesions.",

author = "Ong, {Ju Lynn} and Seghouane, {Abd Krim} and Kevin Osborn",

year = "2008",

doi = "10.1109/DICTA.2008.9",

language = "English",

isbn = "9780769534565",

series = "Proceedings - Digital Image Computing: Techniques and Applications, DICTA 2008",

pages = "287--293",

booktitle = "Proceedings - Digital Image Computing",

note = "Digital Image Computing: Techniques and Applications, DICTA 2008 ; Conference date: 01-12-2008 Through 03-12-2008",

}

Ong, JL, Seghouane, AK & Osborn, K 2008, Mean shape models for polyp detection in ct colonography. in Proceedings - Digital Image Computing: Techniques and Applications, DICTA 2008., 4700033, Proceedings - Digital Image Computing: Techniques and Applications, DICTA 2008, pp. 287-293, Digital Image Computing: Techniques and Applications, DICTA 2008, Canberra, ACT, Australia, 1/12/08. https://doi.org/10.1109/DICTA.2008.9

Mean shape models for polyp detection in ct colonography. / Ong, Ju Lynn; Seghouane, Abd Krim; Osborn, Kevin.
Proceedings - Digital Image Computing: Techniques and Applications, DICTA 2008. 2008. p. 287-293 4700033 (Proceedings - Digital Image Computing: Techniques and Applications, DICTA 2008).

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

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AB - A global representation of polyp and non polyp shapes are constructed following a point distribution model (PDM) as an alternative to current methods which only inspect local shape characteristics at a point on the surface. The decision on whether or not a candidate lesion is a polyp can then be made by comparing the minimum Euclidean distance of the candidate lesion to the constructed mean shapes. The model closer in distance to the candidate lesion is selected to represent that particular lesion - polyp or non polyp. This shape model can also be used to investigate the shape variability of the different lesions detected by constructing an allowable shape domain for each of these lesions.

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U2 - 10.1109/DICTA.2008.9

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SN - 9780769534565

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T2 - Digital Image Computing: Techniques and Applications, DICTA 2008

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Mean shape models for polyp detection in ct colonography

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