Brain volumes characterisation using hierarchical neural networks

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 detec...

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Bibliographic Details
Published in:Artificial intelligence in medicine 2003-07, Vol.28 (3), p.307-322
Main Authors: Di Bona, Sergio, Niemann, Heinrich, Pieri, Gabriele, Salvetti, Ovidio
Format: Article
Language:English
Subjects:
3D density classification
Aged
Algorithms
Artificial intelligence
Artificial neural networks
Brain - anatomy & histology
Brain - pathology
Brain imaging
Expert systems
Female
Health
Hierarchical neural networks
Humans
Image Processing, Computer-Assisted
Imaging, Three-Dimensional
Magnetic Resonance Imaging
Male
Medicine
Middle Aged
Neural networks
Neural Networks (Computer)
Reproducibility of Results
Tomography, X-Ray Computed
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Summary: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.
ISSN:0933-3657
1873-2860
DOI:10.1016/S0933-3657(03)00061-7