ArticleMagma (New York, N.Y.)2018
Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain.
Article in Magma (New York, N.Y.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Magnetic resonance texture alterations in the caudate nucleus following 18 weeks of clozapine treatment in patients with treatment-resistant schizophrenia.Translational psychiatry · 2026Article
- Schizophrenia Detection and Classification: A Systematic Review of the Last Decade.Diagnostics (Basel, Switzerland) · 2024Review
- Evaluation of the corpus callosum using magnetic resonance imaging histogram analysis in autism spectrum disorder.The neuroradiology journal · 2022Article
- Crack Texture Feature Identification of Fiber Reinforced Concrete Based on Deep Learning.Materials (Basel, Switzerland) · 2022Article
- Texture analysis of dorsal striatum in functional neurological (conversion) disorder.Brain imaging and behavior · 2022Article
- Multithreshold Segmentation and Machine Learning Based Approach to Differentiate COVID-19 from Viral Pneumonia.Computational intelligence and neuroscience · 2022Article
- Identification of voxel-based texture abnormalities as new biomarkers for schizophrenia and major depressive patients using layer-wise relevance propagation on deep learning decisions.Psychiatry research. Neuroimaging · 2021Article
- Magnetic resonance imaging histogram analysis of corpus callosum in a functional neurological disorderTurkish journal of medical sciences · 2021Article
- Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning.Computational intelligence and neuroscience · 2020Article
- Visuospatial and Sensory Integration Tasks in Patients With Schizophrenia or Schizoaffective Disorder: Relationship to Body Mass Index and Smoking.Frontiers in psychiatry · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveSchizophrenia (SZ) is a psychiatric disorder that especially affects individuals during their adolescence. There is a need to study the subanatomical regions of SZ brain on magnetic resonance images (MRI) based on morphometry. In this work, an attempt was made to analyze alterations in structure and texture patterns in images of the SZ brain using the level-set method and Laws texture features. MATERIALS AND
methodsT1-weighted MRI of the brain from Center of Biomedical Research Excellence (COBRE) database were considered for analysis. Segmentation was carried out using the level-set method. Geometrical and Laws texture features were extracted from the segmented brain stem, corpus callosum, cerebellum, and ventricle regions to analyze pattern changes in SZ.
resultsThe level-set method segmented multiple brain regions, with higher similarity and correlation values compared with an optimized method. The geometric features obtained from regions of the corpus callosum and ventricle showed significant variation (p < 0.00001) between normal and SZ brain. Laws texture feature identified a heterogeneous appearance in the brain stem, corpus callosum and ventricular regions, and features from the brain stem were correlated with Positive and Negative Syndrome Scale (PANSS) score (p < 0.005).
conclusionA framework of geometric and Laws texture features obtained from brain subregions can be used as a supplement for diagnosis of psychiatric disorders.
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