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

Adran Cyfrifiadureg

Norfolk and Norwich University Hospitals NHS Foundation Trust

Allbwn ymchwil: Cyfraniad at gynhadledd › Papur › adolygiad gan gymheiriaid

18 Wedi eu Llwytho i Lawr (Pure)

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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.

Iaith wreiddiol	Saesneg
Tudalennau	75-80
Nifer y tudalennau	6
Statws	Cyhoeddwyd - 27 Awst 2014
Digwyddiad	2014 Irish Machine Vision and Image Processing - University of Ulster, Londonderry, Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon Hyd: 27 Awst 2014 → 29 Awst 2014

Cynhadledd

Cynhadledd	2014 Irish Machine Vision and Image Processing
Teitl cryno	IMVIP
Gwlad/Tiriogaeth	Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon
Dinas	Londonderry
Cyfnod	27 Awst 2014 → 29 Awst 2014

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

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@conference{4534bd0c62b948b6ade58a67d62641d3,

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",

month = aug,

day = "27",

language = "English",

pages = "75--80",

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

}

Detection and Localisation of Prostate Cancer within the Peripheral Zone using Scoring Algorithm. / Rampun, Yambu Andrik; Malcolm, Paul; Zwiggelaar, Reyer.
2014. 75-80 Papur a gyflwynwyd yn 2014 Irish Machine Vision and Image Processing, Londonderry, Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon.

Allbwn ymchwil: Cyfraniad at gynhadledd › Papur › adolygiad gan gymheiriaid

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

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NDC y CU

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