| Title |
Accurate Automated Detection of Autism Related Corpus Callosum Abnormalities
|
|---|---|
| Published in |
Journal of Medical Systems, May 2010
|
| DOI | 10.1007/s10916-010-9510-3 |
| Pubmed ID | |
| Authors |
Ayman El-Baz, Ahmed Elnakib, Manuel F. Casanova, Georgy Gimel’farb, Andrew E. Switala, Desha Jordan, Sabrina Rainey |
| Abstract |
The importance of accurate early diagnostics of autism that severely affects personal behavior and communication skills cannot be overstated. Neuropathological studies have revealed an abnormal anatomy of the Corpus Callosum (CC) in autistic brains. We propose a new approach to quantitative analysis of three-dimensional (3D) magnetic resonance images (MRI) of the brain that ensures a more accurate quantification of anatomical differences between the CC of autistic and normal subjects. It consists of three main processing steps: (i) segmenting the CC from a given 3D MRI using the learned CC shape and visual appearance; (ii) extracting a centerline of the CC; and (iii) cylindrical mapping of the CC surface for its comparative analysis. Our experiments revealed significant differences (at the 95% confidence level) between 17 normal and 17 autistic subjects in four anatomical divisions, i.e. splenium, rostrum, genu and body of their CCs. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Croatia | 1 | 2% |
| United States | 1 | 2% |
| Brazil | 1 | 2% |
| Unknown | 38 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 17% |
| Professor | 5 | 12% |
| Student > Bachelor | 5 | 12% |
| Student > Master | 5 | 12% |
| Student > Ph. D. Student | 4 | 10% |
| Other | 6 | 15% |
| Unknown | 9 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 6 | 15% |
| Engineering | 5 | 12% |
| Medicine and Dentistry | 5 | 12% |
| Psychology | 5 | 12% |
| Agricultural and Biological Sciences | 2 | 5% |
| Other | 8 | 20% |
| Unknown | 10 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 July 2013.
All research outputs
#18,341,711
of 22,714,025 outputs
Outputs from Journal of Medical Systems
#804
of 1,144 outputs
Outputs of similar age
#85,683
of 95,046 outputs
Outputs of similar age from Journal of Medical Systems
#14
of 16 outputs
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