Brain volumes characterisation using hierarchical neural networks

被引:5
|
作者
Di Bona, S
Niemann, H
Pieri, G
Salvetti, O
机构
[1] Italian Natl Res Council, Inst Informat Sci & Technol, I-56124 Pisa, Italy
[2] Bavarian Res Ctr Knowledge Based Syst, D-91058 Erlangen, Germany
关键词
hierarchical neural networks; artificial neural networks; 3D density classification; brain imaging;
D O I
10.1016/S0933-3657(03)00061-7
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Objective knowledge of tissue density distribution in CT/MRI brain datasets can be related to anatomical or neuro-functional regions for assessing pathologic conditions characterised by slight differences. The process of monitoring illness and its treatment could be then improved by a suitable detection of these variations. In this paper, we present an approach for three-dimensional (3D) classification of brain tissue densities based on a hierarchical artificial neural network (ANN) able to classify the single voxels of the examined datasets. The method developed was tested on case studies selected by an expert neuro-radiologist and consisting of both normal and pathological conditions. The results obtained were submitted for validation to a group of physicians and they judged the system to be really effective in practical applications. (C) 2003 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:307 / 322
页数:16
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