Authors:
Elisabetta Binaghi
;
Massimo Omodei
;
Valentina Pedoia
;
Sergio Balbi
;
Desiree Lattanzi
and
Emanuele Monti
Affiliation:
Insubria University, Italy
Keyword(s):
MRI Segmentation, Brain Tumor Segmentation, Meningioma, Graph Cut, Support Vector Machine.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer-Supported Education
;
Data Manipulation
;
Domain Applications and Case Studies
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image Processing and Artificial Vision Applications
;
Industrial, Financial and Medical Applications
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Support Vector Machines and Applications
;
Theory and Methods
Abstract:
This work focuses the attention on the automatic segmentation of meningioma from multispectral brain Magnetic Resonance imagery. The Authors address the segmentation task by proposing a fully automatic method hierarchically structured in two phases. The preliminary unsupervised phase is based on Graph Cut framework. In the second phase, preliminary segmentation results are refined using a supervised classification based on Support Vector Machine. The overall segmentation procedure is conceived fully automatic and tailored to non-volumetric data characterized by poor inter-slice spacing, in an attempt to facilitate the insertion in clinical practice. The results obtained in this preliminary study are encouraging and prove that the segmentation benefits from the allied use of Graph Cut and Support Vector Machine frameworks.