TY - GEN
T1 - Classification of Structural MRI Images in Adhd Using 3D Fractal Dimension Complexity Map
AU - Wang, Tianyi
AU - Kamata, Sei Ichiro
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant Number 18K11380.
PY - 2019/9
Y1 - 2019/9
N2 - Attention deficit hyperactivity disorder (ADHD) is a common mental-health disorder in adolescent groups. Successful automatic diagnosis of ADHD based on features extracted from magnetic resonance imaging (MRI) data, would provide reference information for treating. Previous researches have shown gray matter (GM) of some anatomical brain structures will increase in ADHD subjects. Fractal analysis has been widely used in texture image processing and fractal dimension is capable of representing intrinsic structural information of images. With large-scale MRI data becoming publicly available, deep-learning methods for ADHD diagnosis become feasible. This paper proposes a novel classification approach using 3D fractal dimension complexity map (FDCM) for ADHD automatic diagnosis. We calculate the Hausdorff fractal dimension of GM density data extracted from structural MRI data. Subsequently, we design a 3 dimensional convolutional neural network (3D-CNN) for extracting features from FDCM then judging ADHD and TDC. Our model is evaluated on the hold-out testing data of the ADHD-200 global competition and performance outperforms previous approaches based on structural MRI data.
AB - Attention deficit hyperactivity disorder (ADHD) is a common mental-health disorder in adolescent groups. Successful automatic diagnosis of ADHD based on features extracted from magnetic resonance imaging (MRI) data, would provide reference information for treating. Previous researches have shown gray matter (GM) of some anatomical brain structures will increase in ADHD subjects. Fractal analysis has been widely used in texture image processing and fractal dimension is capable of representing intrinsic structural information of images. With large-scale MRI data becoming publicly available, deep-learning methods for ADHD diagnosis become feasible. This paper proposes a novel classification approach using 3D fractal dimension complexity map (FDCM) for ADHD automatic diagnosis. We calculate the Hausdorff fractal dimension of GM density data extracted from structural MRI data. Subsequently, we design a 3 dimensional convolutional neural network (3D-CNN) for extracting features from FDCM then judging ADHD and TDC. Our model is evaluated on the hold-out testing data of the ADHD-200 global competition and performance outperforms previous approaches based on structural MRI data.
KW - 3D convolutional neural network
KW - 3D fractal dimension based complexity map
KW - Attention deficit hyperactive disorder
KW - Structural MRI
UR - http://www.scopus.com/inward/record.url?scp=85076813222&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076813222&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2019.8802930
DO - 10.1109/ICIP.2019.8802930
M3 - Conference contribution
AN - SCOPUS:85076813222
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 215
EP - 219
BT - 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PB - IEEE Computer Society
T2 - 26th IEEE International Conference on Image Processing, ICIP 2019
Y2 - 22 September 2019 through 25 September 2019
ER -