Semi-automatic segmentation of the prostate

Reyer Zwiggelaar; Yanong Zhu; Stuart Williams

doi:10.1007/978-3-540-44871-6_128

Semi-automatic segmentation of the prostate

Reyer Zwiggelaar, Yanong Zhu, Stuart Williams

School of Computing Sciences

Research output: Chapter in Book/Report/Conference proceeding › Chapter

39 Citations (Scopus)

Abstract

A semi-automatic method has been developed which segments the prostate in slices of Magnetic Resonance Imaging (MRI) data. The developed approach exploits the characteristics of the anatomical shape of the prostate when represented in a polar transform space. Simple techniques, such as line detection and non-maximum suppression, are used to track the boundary of the prostate. The initial results, based on a small set of data, indicate a good correlation with expert based manual segmentation.

Original language	English
Title of host publication	Pattern Recognition and Image Analysis
Editors	Francisco José Perales, Aurélio J. C. Campilho, Nicolás Pérez de la Blanca, Alberto Sanfeliu
Publisher	Springer Berlin / Heidelberg
Pages	1108-1116
Number of pages	9
Volume	2652
ISBN (Print)	978-3-540-40217-6
DOIs	https://doi.org/10.1007/978-3-540-44871-6_128
Publication status	Published - 2003
Event	1st Iberian Conference - Puerto de Andratx, Mallorca, Spain Duration: 4 Jun 2003 → 6 Jun 2003

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Berlin / Heidelberg

Conference

Conference	1st Iberian Conference
Abbreviated title	IbPRIA 2003
Country/Territory	Spain
City	Puerto de Andratx, Mallorca
Period	4/06/03 → 6/06/03

Access to Document

10.1007/978-3-540-44871-6_128

Cite this

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title = "Semi-automatic segmentation of the prostate",

abstract = "A semi-automatic method has been developed which segments the prostate in slices of Magnetic Resonance Imaging (MRI) data. The developed approach exploits the characteristics of the anatomical shape of the prostate when represented in a polar transform space. Simple techniques, such as line detection and non-maximum suppression, are used to track the boundary of the prostate. The initial results, based on a small set of data, indicate a good correlation with expert based manual segmentation.",

author = "Reyer Zwiggelaar and Yanong Zhu and Stuart Williams",

year = "2003",

doi = "10.1007/978-3-540-44871-6_128",

language = "English",

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publisher = "Springer Berlin / Heidelberg",

pages = "1108--1116",

editor = "Perales, {Francisco Jos{\'e}} and Campilho, {Aur{\'e}lio J. C.} and {de la Blanca}, {Nicol{\'a}s P{\'e}rez} and Alberto Sanfeliu",

booktitle = "Pattern Recognition and Image Analysis",

note = "1st Iberian Conference, IbPRIA 2003 ; Conference date: 04-06-2003 Through 06-06-2003",

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Zwiggelaar, R, Zhu, Y & Williams, S 2003, Semi-automatic segmentation of the prostate. in FJ Perales, AJC Campilho, NP de la Blanca & A Sanfeliu (eds), Pattern Recognition and Image Analysis. vol. 2652, Lecture Notes in Computer Science, Springer Berlin / Heidelberg, pp. 1108-1116, 1st Iberian Conference, Puerto de Andratx, Mallorca, Spain, 4/06/03. https://doi.org/10.1007/978-3-540-44871-6_128

Semi-automatic segmentation of the prostate. / Zwiggelaar, Reyer; Zhu, Yanong; Williams, Stuart.

Pattern Recognition and Image Analysis. ed. / Francisco José Perales; Aurélio J. C. Campilho; Nicolás Pérez de la Blanca; Alberto Sanfeliu. Vol. 2652 Springer Berlin / Heidelberg, 2003. p. 1108-1116 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

T1 - Semi-automatic segmentation of the prostate

AU - Zwiggelaar, Reyer

AU - Zhu, Yanong

AU - Williams, Stuart

PY - 2003

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AB - A semi-automatic method has been developed which segments the prostate in slices of Magnetic Resonance Imaging (MRI) data. The developed approach exploits the characteristics of the anatomical shape of the prostate when represented in a polar transform space. Simple techniques, such as line detection and non-maximum suppression, are used to track the boundary of the prostate. The initial results, based on a small set of data, indicate a good correlation with expert based manual segmentation.

U2 - 10.1007/978-3-540-44871-6_128

DO - 10.1007/978-3-540-44871-6_128

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VL - 2652

T3 - Lecture Notes in Computer Science

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EP - 1116

BT - Pattern Recognition and Image Analysis

A2 - Perales, Francisco José

A2 - Campilho, Aurélio J. C.

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PB - Springer Berlin / Heidelberg

T2 - 1st Iberian Conference

Y2 - 4 June 2003 through 6 June 2003

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