A 3D Voxel Neighborhood Classification Approach within a Multiparametric MRI Classifier for Prostate Cancer Detection

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• 作者：Francesco Rossi (20)
  Alessandro Savino (20)
  Valentina Giannini (21)
  Anna Vignati (21)
  Simone Mazzetti (21)
  Alfredo Benso (20)
  Stefano Di Carlo (20)
  Gianfranco Politano (20)
  Daniele Regge (21)
  
  20. Control and Comp. Engineering Department ; Politecnico di Torino ; Torino ; Italy
  21. Radiology Unit ; Candiolo Cancer Institute FPO ; IRCCS ; Candiolo (Torino) ; Italy
  
• 关键词：Prostate cancer ; magnetic resonance imaging ; support vector machine ; MRI classification
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9043
• 期：1
• 页码：231-239
• 全文大小：348 KB
• 参考文献：1. European Cancer Observatory (ECO), http://eu-cancer.iarc.fr/
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• 作者单位：Bioinformatics and Biomedical Engineering
• 丛书名：978-3-319-16482-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

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.