Population classification based on structural morphometry of cortical sulci
- Resource Type
- Conference
- Authors
- Duchesnay, E.; Roche, A.; Riviere, D.; Papadopoulos, D.; Cointepas, Y.; Mangin, J.-F.
- Source
- 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821) Biomedical Imaging: Nano to Macro Biomedical Imaging: Nano to Macro, 2004. IEEE International Symposium on. :1267-1270 Vol. 2 2004
- Subject
- Bioengineering
Computing and Processing
Signal Processing and Analysis
Spatial databases
Support vector machines
Support vector machine classification
Shape measurement
Image databases
Performance analysis
Automatic testing
System testing
Neuroimaging
Diseases
- Language
This paper describes a classification system discriminating male and female brains from morphometric features of cortical sulci. This system is tested on a database of 143 brains, whose sulci were automatically recognized by an artificial neuroanatomist described before. The curse of dimensionality usually plaguing classification problems is overcome using an iterative feature selection loop. The best classifier built from an optimal set of 54 morphometric features achieves a 96% correct generalization rate during a leave-one-out procedure. This result obtained using a support vector machine classifier is very appealing considering the limitations of the sulcus recognition system.