High- and Low-Level Contextual Modeling for the Detection of Mild Traumatic Brain Injury

  • Anthony Bianchi
  • Bir Bhanu
  • Andre ObenausEmail author
Part of the Computational Biology book series (COBO, volume 22)


Traumatic brain injury (TBI) can lead to long-term neurological decrements. While moderate and severe TBI are readily discernable from current medical imaging modalities, such as computed tomography and magnetic resonance imaging (MRI), mild TBI (mTBI) is difficult to diagnose from current routine imaging. At the present time there no routine computational methods for the evaluation of mild traumatic brain injury (mTBI) from magnetic resonance imaging (MRI). The development of automated analyses has been hindered by the subtle nature of mTBI abnormalities, which appear as low contrast MR regions. One solution to better identify mTBI injuries from MRI is to use high-level and low-level contextual information. We describe methods and results for using high-level contextual features using a Bayesian network that simulated the evolution of the mTBI injury over time. We also utilized low-level context to obtain more spatial (within the brain) information. The low-level context utilized a classifier to identify temporal information which was then integrated into the subsequent time point being evaluated. We found that both low- and high-level context provided novel information about the mTBI injury. These were in good agreement with manual methods. Future work could combine both low- and high-level context to provide more accurate mTBI segmentation. The results reported herein could ultimately lead to better identification of regions of mTBI injury and thus when treatments become available they can be directed for improved therapy.


MRI Automatic detection Lesion Symmetry Classification 



This work was supported in part by the National Science Foundation Integrative Graduate Education and Research Traineeship (IGERT) in Video Bioinformatics (DGE-0903667). Anthony Bianchi is an IGERT Fellow.


  1. 1.
    Faul M, Xu L, Wald MM, Coronado VG (2010) Traumatic brain injury in the United States: emergency department visits, hospitalizations and deaths. Nat Cent Inj Prev Contr, Centers for Disease Control and Prevention, Atlanta, GAGoogle Scholar
  2. 2.
    Vaishnavi S, Rao V, Fann JR (2009) Neuropsychiatric problems after traumatic brain injury: unraveling the silent epidemic. Psychosomatics 50:198–205CrossRefGoogle Scholar
  3. 3.
    Hunter JV, Wilde EA, Tong KA, Holshouser BA (2012) Emerging imaging tools for use with traumatic brain injury research. J Neurotrauma 29:654–671CrossRefGoogle Scholar
  4. 4.
    Benson RR, Gattu R, Sewick B, Kou Z, Zakariah N, Cavanaugh JM, Haacke EM (2012) Detection of hemorrhagic and axonal pathology in mild traumatic brain injury using advanced MRI: implications for neurorehabilitation. Neuro Rehabil 31:261–279Google Scholar
  5. 5.
    VA/DoD (2009) Management of concussion/mild traumatic brain injury. VA/DoD Evid Based Pract: Clin Pract GuideGoogle Scholar
  6. 6.
    Ajao DO, Pop V, Kamper JE, Adami A, Rudobeck E, Huang L, Vlkolinsky R, Hartman RE, Ashwal S, Obenaus A, Badaut J (2012) Traumatic brain injury in young rats leads to progressive behavioral deficits coincident with altered tissue properties in adulthood. J Neurotrauma 29:2060–2074CrossRefGoogle Scholar
  7. 7.
    Konrad C, Geburek AJ, Rist F, Blumenroth H, Fischer B, Husstedt I, Arolt V, Schiffbauer H, Lohmann H (2010) Long-term cognitive and emotional consequences of mild traumatic brain injury. Psychol Med 41:1–15Google Scholar
  8. 8.
    Donovan V, Kim C, Anugerah AK, Coats JS, Oyoyo U, Pardo AC, Obenaus A (2014) Repeated mild traumatic brain injury results in long-term white-matter disruption. J Cereb Blood Flow Metab: Off J Int Soc Cerebr Blood Flow Metab 34(4):715–723Google Scholar
  9. 9.
    Inglese M, Makani S, Johnson G, Cohen BA, Silver JA, Gonen O, Grossman RI (2005) Diffuse axonal injury in mild traumatic brain injury: a diffusion tensor imaging study. J Neurosurg 103:298–303CrossRefGoogle Scholar
  10. 10.
    Niogi SN, Mukherjee P (2010) Diffusion tensor imaging of mild traumatic brain injury. J Head Trauma Rehabil 25:241–255CrossRefGoogle Scholar
  11. 11.
    Donovan V, Bianchi A, Hartman R, Bhanu B, Carson MJ, Obenaus A (2012) Computational analysis reveals increased blood deposition following repeated mild traumatic brain injury. Neuro Image: Clin 1:18–28Google Scholar
  12. 12.
    Colgan NC, Cronin MM, Gobbo OL, O’Mara SM, O’Connor WT, Gilchrist MD (2010) Quantitative MRI analysis of brain volume changes due to controlled cortical impact. J Neurotrauma 27:1265–1274CrossRefGoogle Scholar
  13. 13.
    Metting Z, Rodiger LA, De Keyser J, van der Naalt J (2007) Structural and functional neuroimaging in mild-to-moderate head injury. Lancet Neurol 6:699–710CrossRefGoogle Scholar
  14. 14.
    Obenaus A, Robbins M, Blanco G, Galloway NR, Snissarenko E, Gillard E, Lee S, Curras-Collazo M (2007) Multi-modal magnetic resonance imaging alterations in two rat models of mild neurotrauma. J Neurotrauma 24:1147–1160CrossRefGoogle Scholar
  15. 15.
    Ahmed S, Iftekharuddin KM, Vossough A (2011) Efficacy of texture, shape, and intensity feature fusion for posterior-fossa tumor segmentation in MRI. IEEE Trans Inf Technol Biomed: publ IEEE Eng Med Biol Soc 15:206–213CrossRefGoogle Scholar
  16. 16.
    Kruggel F, Paul JS, Gertz HJ (2008) Texture-based segmentation of diffuse lesions of the brain’s white matter. NeuroImage 39:987–996CrossRefGoogle Scholar
  17. 17.
    Holli KK, Harrison L, Dastidar P, Waljas M, Liimatainen S, Luukkaala T, Ohman J, Soimakallio S, Eskola H (2010) Texture analysis of MR images of patients with mild traumatic brain injury. BMC Med Imaging 10(1):8–17CrossRefGoogle Scholar
  18. 18.
    Holli KK, Waljas M, Harrison L, Liimatainen S, Luukkaala T, Ryymin P, Eskola H, Soimakallio S, Ohman J, Dastidar P (2010) Mild traumatic brain injury: tissue texture analysis correlated to neuropsychological and DTI findings. Acad Radiol 17:1096–1102CrossRefGoogle Scholar
  19. 19.
    Marques O, Barenholtz E, Charvillat V (2011) Context modeling in computer vision: techniques, implications, and applications. Multimed Tools Appl 51:303–339CrossRefGoogle Scholar
  20. 20.
    Divvala S, Hoiem D, Hays J, Efros A, Hebert M (2009) An empirical study of context in object detection. In: Proceedings of IEEE conference on computer vision and pattern recognition, Miami FL, pp 1271–1278Google Scholar
  21. 21.
    Lafferty J, McCallum A, Pereira F (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 10th international conference on machine learning, pp 282–289Google Scholar
  22. 22.
    Tu Z, Bai X (2010) Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Trans Pattern Anal Mach Intell 32:1744–1757CrossRefGoogle Scholar
  23. 23.
    Bianchi A, Bhanu B, Donovan V, Obenaus A (2013) Visual and contextual modeling for the detection of repeated mild traumatic brain injury. IEEE Trans Med Imag 33(1):11–22Google Scholar
  24. 24.
    Vagnozzi R, Tavazzi B, Signoretti S, Amorini AM, Belli A, Cimatti M, Delfini R, Di Pietro V, Finocchiaro A, Lazzarino G (2007) Temporal window of metabolic brain vulnerability to concussions: mitochondrial-related impairment–part I. Neurosurgery 61:379–388CrossRefGoogle Scholar
  25. 25.
    Laird AK (1964) Dynamics of tumor growth. Br J Cancer 13:490–502CrossRefGoogle Scholar
  26. 26.
    Longhi L, Saatman KE, Fujimoto S, Raghupathi R, Meaney DF, Davis J, McMillan BSA, Conte V, Laurer HL, Stein S, Stocchetti N, McIntosh TK (2005) Temporal window of vulnerability to repetitive experimental concussive brain injury. Neurosurgery 56:364–374CrossRefGoogle Scholar
  27. 27.
    Costa L, Cesar R (2001) Shape analysis and classification: theory and practice. CRC, Boca Raton, FLGoogle Scholar
  28. 28.
    Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE conference computer vision and pattern recognition, Kauai, Hawaii, pp 511–518Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Electrical EngineeringUniversity of California RiversideRiversideUSA
  2. 2.Department of PediatricsLoma Linda UniversityLoma LindaUSA

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