Exploring the knowledge contained in neuroimages: statistical discriminant analysis and automatic segmentation of the most significant changes.
OBJECTIVE
The aim of this article is to propose an integrated framework for extracting and describing patterns of disorders from medical images using a combination of linear discriminant analysis and active contour models.


METHODS
A multivariate statistical methodology was first used to identify the most discriminating hyperplane separating two groups of images (from healthy controls and patients with schizophrenia) contained in the input data. After this, the present work makes explicit the differences found by the multivariate statistical method by subtracting the discriminant models of controls and patients, weighted by the pooled variance between the two groups. A variational level-set technique was used to segment clusters of these differences. We obtain a label of each anatomical change using the Talairach atlas.


RESULTS
In this work all the data was analysed simultaneously rather than assuming a priori regions of interest. As a consequence of this, by using active contour models, we were able to obtain regions of interest that were emergent from the data. The results were evaluated using, as gold standard, well-known facts about the neuroanatomical changes related to schizophrenia. Most of the items in the gold standard was covered in our result set.


CONCLUSIONS
We argue that such investigation provides a suitable framework for characterising the high complexity of magnetic resonance images in schizophrenia as the results obtained indicate a high sensitivity rate with respect to the gold standard.
Santos
Paulo E
PE
Electrical Engineering Department, Centro Universitário da Fundação Educacional Inaciana, Av. Humberto de A. Castelo Branco, SBC-SP, Brazil. santosp@ieee.org
Thomaz
Carlos E
CE
dos Santos
Danilo
D
Freire
Rodolpho
R
Sato
João R
JR
Louzã
Mario
M
Sallet
Paulo
P
Busatto
Geraldo
G
Gattaz
Wagner F
WF
D000465:Algorithms; D057205:Automation, Laboratory; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D016022:Case-Control Studies; D003627:Data Interpretation, Statistical; D016002:Discriminant Analysis; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D015233:Models, Statistical; D011237:Predictive Value of Tests; D012559:Schizophrenia / Q000175:diagnosis* / Q000473:pathology; D012680:Sensitivity and Specificity