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Proceedings Paper

Automatic identification of colonic polyp in high-resolution CT images
Author(s): Jamshid Dehmeshki; Hamdan Amin; Wing Wong; Mandana Ebadian Dehkordi; Nahid Kamangari; Mary Roddie; John Costello
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Paper Abstract

Automatic polyp detection is a challenging task as polyps come in different sizes and shape. The detection generally consists of colon segmentation, identification of suspected polyps and classification. Classification involves discriminating polyps from among many suspected regions based on a number of features extracted from the detected regions. This paper presents the work on the first two stages of the detection. For the colon segmentation, the fuzzy connectivity region growing technique is used while for the identification of suspected polyps concave region searching is applied. A rule-based filtering based on 3D volumetric features is used to reduce a large number of non-polyp structures (false positives). The method is fast, robust and validated with a number of high-resolution colon datasets.

Paper Details

Date Published: 12 May 2004
PDF: 8 pages
Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.535339
Show Author Affiliations
Jamshid Dehmeshki, Medicsight Plc. (United Kingdom)
Hamdan Amin, Medicsight Plc. (United Kingdom)
Wing Wong, Medicsight Plc. (United Kingdom)
Mandana Ebadian Dehkordi, Medicsight Plc. (United Kingdom)
Nahid Kamangari, Medicsight Plc. (United Kingdom)
Mary Roddie, New Victoria Hospital (United Kingdom)
John Costello, Cromwell Hospital (United Kingdom)
King's College Hospital (United Kingdom)


Published in SPIE Proceedings Vol. 5370:
Medical Imaging 2004: Image Processing
J. Michael Fitzpatrick; Milan Sonka, Editor(s)

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