Study of Control and Autistic Brain Based on Corpus Callosum Analysis

Study of Control and Autistic Brain Based on Corpus Callosum Analysis
  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-2
Year of Publication : 2022
Authors : W.Z. Loskor, Sharif Ahamed, Tania Akter, Farzana Tasnim
DOI :  10.14445/22315381/IJETT-V70I2P221

How to Cite?

W.Z. Loskor, Sharif Ahamed, Tania Akter, Farzana Tasnim, "Study of Control and Autistic Brain Based on Corpus Callosum Analysis," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 185-194, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I2P221

Abstract
In the present world, autism is a potential threat to child development. It is a non-curable developmental neural disorder that is featured by inconveniences in social interactions. It is marked by impaired communications, restricted and repetitive interests, and behaviours. The malfunctioning corpus callosum (CC) is the main culprit for autism. In this paper, we study CC to differentiate between autistic and control brains. To accomplish the study, image segmentation, edge detection, and morphological operation techniques are introduced. To note, comprehensive experiments on fMRI images demonstrate the efficiency of our work.

Keywords
Autism, Corpus Callosum, Image Segmentation, Edge detection, Morphological operation.

Reference
[1] Tony F. Chan and Luminita A. Vese, Active Contours Without Edges, IEEE Transaction On Image Processing, 10(2) (2001).
[2] Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 17(2) (2002) 825–841.
[3] Renteria-Vazquez, Tiffany, et al.,Social inferences in agenesis of the corpus callosum and autism: Semantic analysis and topic modeling. Journal of Autism and Developmental Disorders, (2021) 1-15.
[4] Loomba, Niharika, et al , Corpus callosum size and homotopic connectivity in Autism spectrum disorder., Psychiatry Research: Neuroimaging ,313 (2021)111301.
[5] Guo, Yurui, et al. Symptom-Related Differential Neuroimaging Biomarkers in Children with Corpus Callosum Abnormalities.Cerebral Cortex., 31(11) (2021) 4916-4932.
[6] Kim, Johanna Inhyang, et al. Classification of Preschoolers with Low-Functioning Autism Spectrum Disorder Using Multimodal MRI Data.Journal of Autism and Developmental Disorders,. (2022) 1-13.
[7] Menon, Ravi S., et al. BOLD based functional MRI at 4 Tesla includes a capillary bed contribution: echo?planar imaging correlates with previous optical imaging using intrinsic signals., Magnetic resonance in medicine 33(3) (1995) 453-459.
[8] V. Caselles, F. Catte, T. Coll, and F. Dibos, A geometric model for active contours in image processing, Numer. Math., 66 (1993) 1-31.
[9] Alexander, A. L., Lee, J. E., Lazar, M., Boudos, R., DuBray, M. B., Oakes, T. R., et al. Diffusion tensor imaging of the corpus callosum in autism. Neuroimage, 34(1) (2007) 61–73.
[10] Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., & Gerig, G. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage, 31(3) (2006) 1116–1128.
[11] Amaral, D. G., Schumann, C. M., & Nordahl, C. W. Neuroanatomy of autism. Trends in Neurosciences, 31(3) (2008) 137–145.
[12] Valk, S. L., Di Martino, A., Milham, M. P., & Bernhardt, B. C. Multicenter mapping of structural network alterations in autism. Human Brain Mapping, 36 (2015) 2364–2373.
[13] H. Zhao, T. Chan, B. Merriman, and S. Osher, A variational level set approach to multiphase motion, J. Comp. Phys., 127 (1996) 179-195.
[14] M. Kass, A.Witkin, and D. Terzopoulos, Snakes: active contour models, Int’l J. Comp. Vis., 1 (1987) 321-331.