Detection and Localisation of Prostate Cancer within the Peripheral Zone using Scoring Algorithm

Yambu Andrik Rampun; Paul Malcolm; Reyer Zwiggelaar

Detection and Localisation of Prostate Cancer within the Peripheral Zone using Scoring Algorithm

Yambu Andrik Rampun, Paul Malcolm, Reyer Zwiggelaar

Department of Computer Science

Norfolk & Norwich University Hospital

Research output: Contribution to conference › Paper › peer-review

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Abstract

The paper presents a novel computer aided diagnosis method for prostate cancer detection within the prostate’s peripheral zone using a combination of different image features and grey level histogram analysis. The peripheral zone is subdivided into four regions and a scoring algorithm is employed to determine the most cancerous sub region based on specific metrics. The initial evaluation of this method is based on 200 MRI images from 40 patients and we achieved 89% accuracy with 0.89 and 0.88 sensitivity and specificity,
respectively.

Original language	English
Pages	75-80
Number of pages	6
Publication status	Published - 27 Aug 2014
Event	2014 Irish Machine Vision and Image Processing - University of Ulster, Londonderry, United Kingdom of Great Britain and Northern Ireland Duration: 27 Aug 2014 → 29 Aug 2014

Conference

Conference	2014 Irish Machine Vision and Image Processing
Abbreviated title	IMVIP
Country/Territory	United Kingdom of Great Britain and Northern Ireland
City	Londonderry
Period	27 Aug 2014 → 29 Aug 2014

Keywords

Prostate Cancer Detection
MRI
Prostate Cancer Localisation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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title = "Detection and Localisation of Prostate Cancer within the Peripheral Zone using Scoring Algorithm",

abstract = "The paper presents a novel computer aided diagnosis method for prostate cancer detection within the prostate{\textquoteright}s peripheral zone using a combination of different image features and grey level histogram analysis. The peripheral zone is subdivided into four regions and a scoring algorithm is employed to determine the most cancerous sub region based on specific metrics. The initial evaluation of this method is based on 200 MRI images from 40 patients and we achieved 89% accuracy with 0.89 and 0.88 sensitivity and specificity,respectively.",

keywords = "Prostate Cancer Detection, MRI, Prostate Cancer Localisation",

author = "Rampun, {Yambu Andrik} and Paul Malcolm and Reyer Zwiggelaar",

year = "2014",

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note = "2014 Irish Machine Vision and Image Processing, IMVIP ; Conference date: 27-08-2014 Through 29-08-2014",

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Detection and Localisation of Prostate Cancer within the Peripheral Zone using Scoring Algorithm. / Rampun, Yambu Andrik; Malcolm, Paul; Zwiggelaar, Reyer.
2014. 75-80 Paper presented at 2014 Irish Machine Vision and Image Processing, Londonderry, United Kingdom of Great Britain and Northern Ireland.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Detection and Localisation of Prostate Cancer within the Peripheral Zone using Scoring Algorithm

AU - Rampun, Yambu Andrik

AU - Malcolm, Paul

AU - Zwiggelaar, Reyer

PY - 2014/8/27

Y1 - 2014/8/27

N2 - The paper presents a novel computer aided diagnosis method for prostate cancer detection within the prostate’s peripheral zone using a combination of different image features and grey level histogram analysis. The peripheral zone is subdivided into four regions and a scoring algorithm is employed to determine the most cancerous sub region based on specific metrics. The initial evaluation of this method is based on 200 MRI images from 40 patients and we achieved 89% accuracy with 0.89 and 0.88 sensitivity and specificity,respectively.

AB - The paper presents a novel computer aided diagnosis method for prostate cancer detection within the prostate’s peripheral zone using a combination of different image features and grey level histogram analysis. The peripheral zone is subdivided into four regions and a scoring algorithm is employed to determine the most cancerous sub region based on specific metrics. The initial evaluation of this method is based on 200 MRI images from 40 patients and we achieved 89% accuracy with 0.89 and 0.88 sensitivity and specificity,respectively.

KW - Prostate Cancer Detection

KW - MRI

KW - Prostate Cancer Localisation

M3 - Paper

SP - 75

EP - 80

T2 - 2014 Irish Machine Vision and Image Processing

Y2 - 27 August 2014 through 29 August 2014

ER -

Detection and Localisation of Prostate Cancer within the Peripheral Zone using Scoring Algorithm

Abstract

Conference

Keywords

UN SDGs

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