A NEW IMAGE ANALYSIS APPROACH FOR AUTOMATIC CLASSIFICATION OF AUTISTIC BRAINS

This publication is included in the Autistic Autism Scholarship Project. one author of this publication is identified as autistic in the project.

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El-baz, A., Casanova, M., Gimel'farb, G., Mott, M., & Switwala, A. (2007). A NEW IMAGE ANALYSIS APPROACH FOR AUTOMATIC CLASSIFICATION OF AUTISTIC BRAINS. 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 352-355. https://doi.org/10.1109/isbi.2007.356861

Publication date: 1 Jan 2007 Added to AutiHub: 14 Sep 2026 Type: Other Article language: English
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Authors

Publication authors
5
Publication authors identified as autistic
1 / 5 (20.0%)
  1. Ayman El-baz
  2. Manuel Casanova *
  3. Georgy Gimel'farb
  4. Meghan Mott
  5. Andrew Switwala

Abstract

Autism is a developmental disorder characterized by social deficits, impaired communication, and restricted and repetitive patterns of behavior. Recent neuropathological studies of autism have revealed abnormal anatomy of the cerebral white matter (CWM) in autistic brains. In this paper we introduced a novel approach to classify autistic from normal subjects based on a new shape analysis of cerebral white matter gyrifications for both normal and autistic subjects. The proposed shape analysis technique consists of three main steps. The first step is to segment cerebral white matter from proton density MRI images using a priorly learned visual appearance model for the 3D cerebral white matter in order to control the evolution of deformable boundaries. The appearance prior is modeled with a translation and rotation invariant Markov-Gibbs random field of voxel intensities with a pairwise interaction model. The second step is to extract the gyrifications of cerebral white matter from the segmented cerebral white matter. The last step is to perform shape analysis to quantify the thickness of the extracted cerebral white matter gyrifications for both autistic and normal subjects. The preliminary results of the proposed image analysis has yielded promising results that would, in the near future, supplement the use of current technologies for diagnosing autism

Bibliography cited by this reference

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Cited bibliography overview

These indicators describe the bibliography cited by this publication. An author name is counted each time it appears in one cited reference, so the same person can be counted more than once. Names not yet linked to an author already present in AutiHub are treated as unknown, not as non-autistic. Last computed: 14 Sep 2026 14:22.

Cited references
13
With a DOI
13
Without a DOI, from raw bibliography text
0
0 / 13 (0.0%) cited references include at least one author identified as autistic.
References with data to complete
0 / 13 (0.0%)
References with detected author names
13 / 13 (100.0%)
Without detected author names
0
Without a structured title
0
Without a stable identifier
0
References with raw author names still to review
0
References with external metadata lookup issues
0
These indicators apply to cited references displayed on this page, after technical duplicates have been merged. A reference without a DOI can still support author statistics when a title and author names are available.
Author names detected in the cited bibliography
51
From DOI or external metadata
51
From validated raw bibliography text
0
Raw names already validated
0
Raw names still to review
0
11 / 51 (21.6%) names are linked to an author already present in AutiHub. 40 / 51 (78.4%) names are not yet linked.
Names linked to a person identified as autistic
0 / 51 (0.0%)
Calculated across all author names detected in the cited bibliography. Among names linked to an author already present in AutiHub: 0 / 11 (0.0%). Distinct people identified as autistic: 0 / 48 (0.0%).
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