A multilevel neural network model for density volumes classification
- Resource Type
- Conference
- Authors
- Di Bona, S.; Pieri, G.; Salvetti, O.
- Source
- ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat. Image and signal processing and analysis Image and Signal Processing and Analysis, 2001. ISPA 2001. Proceedings of the 2nd International Symposium on. :213-218 2001
- Subject
- Signal Processing and Analysis
Computing and Processing
Neural networks
Pathology
Biological neural networks
Brain
Magnetic resonance imaging
Monitoring
Information processing
Councils
Information analysis
Anatomical structure
- Language
The accurate detection of tissue density variation in CT/MRI brain datasets can be useful for analysing and monitoring pathologies with slight differences. In fact, the objective knowledge of density distribution can be related to anatomical structures and therefore the process of monitoring illness and its treatment can be improved. In this paper, we present an approach for the classification of tissue density in three dimensional brain tomographic scans. The proposed approach is based on a hierarchical neural network model able to classify the single voxels of the examined datasets. The approach has been evaluated on both normal and pathological cases selected by an expert neuroradiologist as study cases. The results have shown that the method has a good effectiveness in practical applications and that it can be used for designing a full 3D instrument suitable for supporting the analysis of disease diagnosis and follow-up.