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Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain

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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.

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Acknowledgements

ML is receiving Anna Centenary Research Fellowship from Anna University (CFR/ACRF/2015/20, Dated: 21.01.2015) for her research work.

Funding

This study has not received any funding.

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Authors and Affiliations

  1. Department of Electronics Engineering, Madras Institute of Technology, Chromepet, Chennai, India

    Manohar Latha & Ganesan Kavitha

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  1. Manohar Latha
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  2. Ganesan Kavitha
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Contributions

ML was responsible for data collection, implementation, and data analysis; GK was responsible for protocol/project development of the frame work.

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Correspondence to Manohar Latha.

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The authors declare that they have no conflict of interest.

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This article does not contain any studies performed with animals.

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This study includes the images from publicly available database and the database has been cited.

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Latha, M., Kavitha, G. Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain. Magn Reson Mater Phy 31, 483–499 (2018). https://doi.org/10.1007/s10334-018-0674-z

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  • DOI: https://doi.org/10.1007/s10334-018-0674-z

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