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Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies pp 161–185Cite as

Shape-Based Detection of Cortex Variability for More Accurate Discrimination Between Autistic and Normal Brains

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Abstract

Autism is a complex developmental disability that typically appears during the first 3 years of life, and is the result of a neurological disorder that affects the normal functioning of the brain, impacting development in the areas of social interaction and communication skills. Early detection allows for treatments to be attempted, thus minimizing the impact of the autism on the individual. Given currently available diagnostic instruments, autism and other pervasive developmental disorders are difficult to detect in very young children. While shape based statistical analysis methods for autism are still in their early stages, current results show positive outlooks on the ability to detect differences between autistic and non-autistic patients. A framework is proposed that is capable of taking two-dimensional images from a standard medical scanner, and be able to construct a three-dimensional representation of the object and examine it through combination of its weighted linear spherical harmonics. The desired outcome is that a distinction can be made between the analysis of autistic and non-autistic brain data. The reconstruction analysis process involves linearly combining spherical harmonics of the corresponding mesh. It was expected that due to the complexity of the brain of an autistic subject it would require more iterations of reconstruction to reach convergence of the same error level as compared to the brain of a non-autistic subject. This was confirmed by the data. Using this method of analyzing the data a significant difference can be demonstrated between groups of examined subjects. The research clearly demonstrates that the non-autistic subjects’ data converges both faster and with a lower rate of error level than the data taken from a person with autism.

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Author information

Authors and Affiliations

  1. Bioimaging Laboratory, Department of Bioengineering, University of Louisville, 304 Lutz Building, Louisville, KY, 40292, USA

    Ayman El-Baz

Authors
  1. Matthew Nitzken
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  2. Manuel F. Casanova
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  3. Fahmi Khalifa
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  4. Guela Sokhadze
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  5. Ayman El-Baz
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Corresponding author

Correspondence to Ayman El-Baz .

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Editors and Affiliations

  1. , Department of Bioengineering, University of Louisville, Louisville, 40208, USA

    Ayman S. El-Baz

  2. School of Engineering, Ngee Ann Polytechnic, Clementi Road 535, Singapore, 599489, Singapore

    Rajendra Acharya U

  3. , Department of Biomedical Engineering, Columbia University, New York, 10027, New York, USA

    Andrew F. Laine

  4. Biomedical Technologies, Inc., Denver, Colorado, USA

    Jasjit S. Suri

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Nitzken, M., Casanova, M.F., Khalifa, F., Sokhadze, G., El-Baz, A. (2011). Shape-Based Detection of Cortex Variability for More Accurate Discrimination Between Autistic and Normal Brains. In: El-Baz, A., Acharya U, R., Laine, A., Suri, J. (eds) Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8204-9_7

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