Detection of Prostate Abnormality within the Peripheral Zone using Local Peak Information

Andrik Rampun, Paul Malcolm, Reyer Zwiggelaar

Abstract

In this paper, a fully automatic method is proposed for the detection of prostate cancer within the peripheral zone. The method starts by filtering noise in the original image followed by feature extraction and smoothing which is based on the Discrete Cosine Transform. Next, we identify the peripheral zone area using a quadratic equation and divide it into left and right regions. Subsequently, peak detection is performed on both regions. Finally, we calculate the percentage similarity and Ochiai coefficients to decide whether abnormality occurs. The initial evaluation of the proposed method is based on 90 prostate MRI images from 25 patients and 82.2% (sensitivity/specificity: 0.81/0.84) of the slices were classified correctly with 8.9% false negative and false positive results.

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Paper Citation


in Harvard Style

Rampun A., Malcolm P. and Zwiggelaar R. (2014). Detection of Prostate Abnormality within the Peripheral Zone using Local Peak Information . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 510-519. DOI: 10.5220/0004762905100519


in Bibtex Style

@conference{icpram14,
author={Andrik Rampun and Paul Malcolm and Reyer Zwiggelaar},
title={Detection of Prostate Abnormality within the Peripheral Zone using Local Peak Information},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={510-519},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004762905100519},
isbn={978-989-758-018-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Detection of Prostate Abnormality within the Peripheral Zone using Local Peak Information
SN - 978-989-758-018-5
AU - Rampun A.
AU - Malcolm P.
AU - Zwiggelaar R.
PY - 2014
SP - 510
EP - 519
DO - 10.5220/0004762905100519