Classification of Structural MRI Images in Adhd Using 3D Fractal Dimension Complexity Map

Tianyi Wang, Sei Ichiro Kamata

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
出版社IEEE Computer Society
ページ215-219
ページ数5
ISBN(電子版)9781538662496
DOI
出版ステータスPublished - 2019 9
イベント26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China
継続期間: 2019 9 222019 9 25

出版物シリーズ

名前Proceedings - International Conference on Image Processing, ICIP
2019-September
ISSN(印刷版)1522-4880

Conference

Conference26th IEEE International Conference on Image Processing, ICIP 2019
CountryTaiwan, Province of China
CityTaipei
Period19/9/2219/9/25

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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