TY - GEN
T1 - A 3D voxel neighborhood classification approach within a multiparametric MRI classifier for prostate cancer detection
AU - Rossi, Francesco
AU - Savino, Alessandro
AU - Giannini, Valentina
AU - Vignati, Anna
AU - Mazzetti, Simone
AU - Benso, Alfredo
AU - Di Carlo, Stefano
AU - Politano, Gianfranco
AU - Regge, Daniele
PY - 2015
Y1 - 2015
N2 - Prostate Magnetic Resonance Imaging (MRI) is one of the most promising approaches to facilitate prostate cancer diagnosis. The effort of research community is focused on classification techniques of MR images in order to predict the cancer position and its aggressiveness. The reduction of False Negatives (FNs) is a key aspect to reduce mispredictions and to increase sensitivity. In order to deal with this issue, the most common approaches add extra filtering algorithms after the classification step; unfortunately, this solution increases the prediction time and it may introduce errors. The aim of this study is to present a methodology implementing a 3D voxel-wise neighborhood features evaluation within a Support Vector Machine (SVM) classification model. When compared with a common single-voxel-wise classification, the presented technique increases both specificity and sensitivity of the classifier, without impacting on its performances. Different neighborhood sizes have been tested to prove the overall good performance of the classification.
AB - Prostate Magnetic Resonance Imaging (MRI) is one of the most promising approaches to facilitate prostate cancer diagnosis. The effort of research community is focused on classification techniques of MR images in order to predict the cancer position and its aggressiveness. The reduction of False Negatives (FNs) is a key aspect to reduce mispredictions and to increase sensitivity. In order to deal with this issue, the most common approaches add extra filtering algorithms after the classification step; unfortunately, this solution increases the prediction time and it may introduce errors. The aim of this study is to present a methodology implementing a 3D voxel-wise neighborhood features evaluation within a Support Vector Machine (SVM) classification model. When compared with a common single-voxel-wise classification, the presented technique increases both specificity and sensitivity of the classifier, without impacting on its performances. Different neighborhood sizes have been tested to prove the overall good performance of the classification.
KW - Magnetic resonance imaging
KW - MRI classification
KW - Prostate cancer
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=84944453422&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84944453422&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84944453422
SN - 9783319164823
VL - 9043
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 231
EP - 239
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
T2 - 3rd International Work Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2015
Y2 - 15 April 2015 through 17 April 2015
ER -