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

Tianyi Wang, Sei Ichiro Kamata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PublisherIEEE Computer Society
Pages215-219
Number of pages5
ISBN (Electronic)9781538662496
DOIs
Publication statusPublished - 2019 Sep
Event26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China
Duration: 2019 Sep 222019 Sep 25

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2019-September
ISSN (Print)1522-4880

Conference

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

Keywords

  • 3D convolutional neural network
  • 3D fractal dimension based complexity map
  • Attention deficit hyperactive disorder
  • Structural MRI

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Wang, T., & Kamata, S. I. (2019). Classification of Structural MRI Images in Adhd Using 3D Fractal Dimension Complexity Map. In 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings (pp. 215-219). [8802930] (Proceedings - International Conference on Image Processing, ICIP; Vol. 2019-September). IEEE Computer Society. https://doi.org/10.1109/ICIP.2019.8802930