ArticleThe neuroradiology journal2022
Evaluation of the corpus callosum using magnetic resonance imaging histogram analysis in autism spectrum disorder.
Article in The neuroradiology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Corpus callosum texture analysis: a different perspective approach for diagnosing multiple sclerosis.Neuroradiology · 2025Article
- The Adverse Effects and Use of Bevacizumab in Patients with Glioblastoma: A Systematic Review and Meta-Analysis.Pharmaceuticals (Basel, Switzerland) · 2025Review
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5 authors.
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Abstract
backgroundHistogram analysis is a texture analysis method that can be used in medical images. Quantitative values of the intensity of images can be obtained with histogram analysis. It aimed to evaluate corpus callosum in magnetic resonance images (MRIs) using histogram analysis of pediatric patients with autism spectrum disorder (ASD) to compare them with healthy controls.
methodsThis study included 29 children with ASD and 29 healthy children with normal brain MRI. High-resolution three-dimensional turbo field echo images were obtained with a 1.5 T scanner device for brain magnetic resonance imaging. On the corpus callosum in the sagittal T1-weighted images obtained, mean gray level density (mean), the standard deviation, median, minimum, maximum, entropy, variance, skewness, kurtosis, uniformity, size % L, size % M, size % U, and percentile parameters were measured.
resultsIn ASD patients, mean, standard deviation, maximum, median, variance, entropy, 25%, 75%, 90%, 97%, and 99% values were found to be lower than the control group, and size % U value was higher. In addition, the corpus callosum area was significantly lower in the ASD compared to the controls.
conclusionAccording to our study, corpus callosum of patients with ASD showed differences compared to healthy controls by histogram analysis, even though they were seen as normal in brain MRI. We think that histogram analysis can be used to evaluate possibly affected areas of brain in ASD patients.
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