TY - JOUR
T1 - Computer-aided diagnosis
T2 - Detection and localization of prostate cancer within the peripheral zone
AU - Rampun, Yambu Andrik
AU - Chen, Zhili
AU - Malcolm, Paul
AU - Tiddeman, Bernie
AU - Zwiggelaar, Reyer
N1 - This is the peer reviewed version of the following article: Rampun, Y. A., Chen, Z., Malcolm, P., Tiddeman, B., & Zwiggelaar, R. (2015). Computer-aided diagnosis: detection and localization of prostate cancer within the peripheral zone. International Journal for Numerical Methods in Biomedical Engineering., which has been published in final form at http://dx.doi.org/doi:10.1002/cnm.2745. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
PY - 2016/4/27
Y1 - 2016/4/27
N2 - We propose a methodology for prostate cancer detection and localization within the peripheral zone based on combining multiple segmentation techniques. We extract four image features using Gaussian and median filters. Subsequently, we use each image feature separately to generate binary segmentations. Finally, we take the intersection of all four binary segmentations, incorporating a model of the peripheral zone, and perform erosion to remove small false-positive regions. The initial evaluation of this method is based on 275 MRI images from 37 patients, and 86% of the slices were classified correctly with 87% and 86% sensitivity and specificity achieved, respectively. This paper makes two contributions: firstly, a novel computer-aided diagnosis approach, which is based on combining multiple segmentation techniques using only a small number of simple image features, and secondly, the development of the proposed method and its application in prostate cancer detection and localization using a single MRI modality with the results comparable with the state-of-the-art multimodality and advanced computer vision methods in the literature. Copyright © 2015 John Wiley & Sons, Ltd
AB - We propose a methodology for prostate cancer detection and localization within the peripheral zone based on combining multiple segmentation techniques. We extract four image features using Gaussian and median filters. Subsequently, we use each image feature separately to generate binary segmentations. Finally, we take the intersection of all four binary segmentations, incorporating a model of the peripheral zone, and perform erosion to remove small false-positive regions. The initial evaluation of this method is based on 275 MRI images from 37 patients, and 86% of the slices were classified correctly with 87% and 86% sensitivity and specificity achieved, respectively. This paper makes two contributions: firstly, a novel computer-aided diagnosis approach, which is based on combining multiple segmentation techniques using only a small number of simple image features, and secondly, the development of the proposed method and its application in prostate cancer detection and localization using a single MRI modality with the results comparable with the state-of-the-art multimodality and advanced computer vision methods in the literature. Copyright © 2015 John Wiley & Sons, Ltd
KW - prostate cancer detection
KW - MRI
KW - prostate cancer localization
UR - http://hdl.handle.net/2160/30461
U2 - 10.1002/cnm.2745
DO - 10.1002/cnm.2745
M3 - Article
C2 - 26313267
SN - 2040-7939
VL - 32
JO - International Journal for Numerical Methods in Biomedical Engineering
JF - International Journal for Numerical Methods in Biomedical Engineering
IS - 5
M1 - e02745
ER -