Manohar Latha1, Ganesan Kavitha2. 1. Department of Electronics Engineering, Madras Institute of Technology, Chromepet, Chennai, India. lathakaran@gmail.com. 2. Department of Electronics Engineering, Madras Institute of Technology, Chromepet, Chennai, India.
Abstract
OBJECTIVE: Schizophrenia (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 METHODS: T1-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. RESULTS: The 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). CONCLUSION: A framework of geometric and Laws texture features obtained from brain subregions can be used as a supplement for diagnosis of psychiatric disorders.
OBJECTIVE:Schizophrenia (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 METHODS: T1-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. RESULTS: The 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). CONCLUSION: A framework of geometric and Laws texture features obtained from brain subregions can be used as a supplement for diagnosis of psychiatric disorders.
Authors: Christian Gaser; Igor Nenadic; Bradley R Buchsbaum; Erin A Hazlett; Monte S Buchsbaum Journal: Am J Psychiatry Date: 2004-01 Impact factor: 18.112
Authors: A I Korda; A Ruef; S Neufang; C Davatzikos; S Borgwardt; E M Meisenzahl; N Koutsouleris Journal: Psychiatry Res Neuroimaging Date: 2021-05-16 Impact factor: 2.493
Authors: Tyler S Vanderhoof; Tamara V Gurvits; Julie E Baker-Nolan; David Borsook; Igor Elman Journal: Front Psychiatry Date: 2018-10-16 Impact factor: 4.157