Authors:
Murilo Costa de Barros
1
;
Kaue Duarte
2
;
Chia-Jui Hsu
3
;
Wang-Tso Lee
4
and
Marco Garcia de Carvalho
1
Affiliations:
1
Computing Visual Laboratory, School of Technology - UNICAMP, R. Paschoal Marmo, 1888 - Jd. Nova Itália, 13484-332 - Limeira, São Paulo, Brazil
;
2
Vascular Imaging Laboratory, Calgary University,2500 University Dr NW, Calgary, AB T2N 1N4, Canada
;
3
Department of Pediatrics, National Taiwan University Children’s Hospital, Taiwan
;
4
Department of Pediatrics, National Taiwan University Hospital Hsinchu Branch, Hsinchu, 300001, Taiwan
Keyword(s):
Tourette Syndrome, GLCM, Texture Feature, Shape Feature, Image Processing, Classification, Machine Learning, MRI.
Abstract:
Tourette Syndrome (TS) is a neuropsychiatric disorder characterized by the presence of involuntary motor and vocal tics, with its etiology suggesting a strong and complex genetic basis. The detection of TS is mainly performed clinically, but brain imaging provides additional insights about anatomical structures. Interpreting brain patterns is challenging due to the complexity of the texture and shape of the anatomical regions. This study compares three-dimensional texture and shape features using Gray-Level Co-occurrence Matrix and Scale-Invariant Heat Kernel Signature. These features are analyzed in the context of TS classification (via Support Vector Machines), focusing on anatomical regions believed to be associated with TS. The evaluation is performed on structural Magnetic Resonance (MR) images of 68 individuals (34 TS patients and 34 healthy subjects). Results show that shape features achieve 92.6% accuracy in brain regions like the right thalamus and accumbens area, while text
ure features reach 73.5% accuracy in regions such as right putamen and left thalamus. Majority voting ensembles using shape features obtain 96% accuracy, with texture features achieving 79.4%. These findings highlight the influence of subcortical regions in the limbic system, consistent with existing literature on TS.
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