A multidimensional feature space for automatic classification of autism spectrum disorders (ASD)

Javier Almeida; Nelson Velasco; Eduardo Romero

doi:10.1117/12.2256952

26 January 2017 A multidimensional feature space for automatic classification of autism spectrum disorders (ASD)

Javier Almeida, Nelson Velasco, Eduardo Romero

Proceedings Volume 10160, 12th International Symposium on Medical Information Processing and Analysis; 101600X (2017) https://doi.org/10.1117/12.2256952
Event: 12th International Symposium on Medical Information Processing and Analysis, 2016, Tandil, Argentina

Abstract

Autism Spectrum Disorder (ASD) is a very complex neuro-developmental entity characterized by a wide range of signs. The high variability of reported anatomical changes has arisen the interest of the community to characterize the different patterns of the disorder. Studies so far have focused on measuring the volume of the cerebral cortex as well as the inner brain regions of the brain, and some studies have described consistent changes. This paper presents an automatic method that separates cases with autism from controls in a population between 18 to 35 years extracted from the open database Autism Brain Imaging Data Exchange (ABIDE). The method starts by segmenting a new case, using the delineations associated to the template MNI152. For doing so, the template is non rigidly registered to the input brain. Once these cortical and sub-cortical regions are available, each region is characterized by the histogram of intensities which is normalized. The Kullback-Leibler distance is used as a metric for training a binary SVM classifier, region per region. The highest discrimination values were found for the Right Superior Temporal Gyrus, region which the Area is Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve was 0.67.

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Javier Almeida, Nelson Velasco, and Eduardo Romero "A multidimensional feature space for automatic classification of autism spectrum disorders (ASD)", Proc. SPIE 10160, 12th International Symposium on Medical Information Processing and Analysis, 101600X (26 January 2017); https://doi.org/10.1117/12.2256952

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