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.

A 3D voxel neighborhood classification approach within a multiparametric MRI classifier for prostate cancer detection / Rossi, Francesco; Savino, Alessandro; Giannini, V.; Vignati, A.; Mazzetti, S.; Benso, Alfredo; DI CARLO, Stefano; Politano, GIANFRANCO MICHELE MARIA; Regge, D.. - STAMPA. - 9043:(2015), pp. 231-239. (Intervento presentato al convegno Third International Conference on Bioinformatics and Biomedical Engineering (IWBBIO) tenutosi a Granada, ES nel 15-17 Apr. 2015) [10.1007/978-3-319-16483-0_24].

A 3D voxel neighborhood classification approach within a multiparametric MRI classifier for prostate cancer detection

ROSSI, FRANCESCO;SAVINO, ALESSANDRO;Giannini, V.;BENSO, Alfredo;DI CARLO, STEFANO;POLITANO, GIANFRANCO MICHELE MARIA;
2015

Abstract

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.
2015
9783319164823
9783319164830
File in questo prodotto:
File Dimensione Formato  
2599159_Accepted_Version.pdf

accesso aperto

Descrizione: Full paper
Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 1.89 MB
Formato Adobe PDF
1.89 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2599159
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo