An Automated Framework for Brain Tumour Class Detection

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    With the significant growth in medical imaging techniques and the demand for better processing of medical information, the mandate of automation in disease detection is also increasing. In the modern time, the nature of the diseases has also changed. The highly mortal diseases are becoming difficult to detect due to the high involvements of medical individual and high dependency of human knowledges. The human knowledge is prone to error and often criticized for longer time delay for processing information in disease detections. Thus, the demand from the modern computing and implementation based computational algorithms are to automate the medical disease detection processes with greater accuracy. One such disease with superior mortal rate is brain tumours or cancerous growth in the brain tissues. The regular medical practice approaches have demonstrated the challenges in detection of the tumours and more so the nature of the tumours. Ill detection of the tumour type or the shape of the tumour or the size of the tumours can lead to life threats. Thus, the need for automation in detection is the most expected form of replacements in place of manual diagnosis. Another challenge is the available data formats for such disease reports. The available reports for brain tumour are only in the form of magnetic resonance images or MR Images. The MR Images can cause higher obstacles for further processing as due to the capture process of the patient data. Often, it is observed that the noise present in the MR images makes the processing vulnerable in accuracy. A number of parallel research outcomes have demonstrated significant outcomes of detection of available tumours in the human brain using segmentation methods. Nonetheless, all parallel attempts are criticized for not able to model the growth or the nature of the tumours presents in the human brain. Thus, this work proposes a novel automated framework for detection of tumour types by deploying progressive segmentation and model the growth stages based on features. The parallel outcomes have outrun on detection accuracy due to the use of standard segmentation methods, which is designed for generic image processing and bound not to match the specificity of medical image processing. Thus, this work introduces a novel segmentation method, which is progressive in nature for higher accuracy. This work also outcomes into an automated feature extraction model for brain tumours. The major contribution of the work is to determine the nature of tumour and a sustainable prediction model for tumour stages inside the human brain. The work demonstrates high accuracy for correct detection and prediction of the patient’s life threats in in real time order to take timely medication for making the precious human life more precious.

     

     


  • Keywords


    Progressive Segmentation; Modelling Tumour Features; Tumour – Classifications & Detections; MR Image Analysis; Image Based Modelling.

  • References


      [1] K. L. Bigos, A. R. Hariri, D. R. Weinberger, Neuroimaging Genetics: Principles and Practices, Oxford, U.K.:Oxford Univ. Press, 2016. https://doi.org/10.1093/med/9780199920211.001.0001.

      [2] S. Herculano-Houzel, "The human brain in numbers: A linearly scaled-up primate brain", Frontiers Hum. Neurosci., vol. 3, pp. 31, Nov. 2009. https://doi.org/10.3389/neuro.09.031.2009.

      [3] Brain Facts and Answers, Jan. 2018, [online] Available: http://www.disabled-world.com/artman/publish/brain-facts.shtml.

      [4] L. M. De Angelis, "Brain tumors", New England J. Med., vol. 344, pp. 114-123, Jan. 2001. https://doi.org/10.1056/NEJM200101113440207.

      [5] M. L. Goodenberger, R. B. Jenkins, "Genetics of adult glioma", Cancer Genet., vol. 205, no. 12, pp. 613-621, 2012. https://doi.org/10.1016/j.cancergen.2012.10.009.

      [6] J. Meng, V. Agrahari, I. Youm, "Advances in targeted drug delivery approaches for the central nervous system tumors: The inspiration of nanobiotechnology", J. Neuroimmune Pharmacol., vol. 12, no. 1, pp. 84-98, 2016. https://doi.org/10.1007/s11481-016-9698-1.

      [7] D. N. Louis et al., "The 2007 WHO classification of Tumours of the central nervous system", Acta Neuropathol., vol. 114, no. 2, pp. 97-109, 2007. https://doi.org/10.1007/s00401-007-0243-4.

      [8] E. B. Claus, P. M. Black, "Survival rates and patterns of care for patients diagnosed with supratentorial low-grade gliomas", Cancer, vol. 106, no. 6, pp. 1358-1363, 2006. https://doi.org/10.1002/cncr.21733.

      [9] K. A. Jaeckle et al., "Transformation of low grade glioma and correlation with outcome: An NCCTG database analysis", J. Neuro-Oncol., vol. 104, no. 1, pp. 253-259, 2011. https://doi.org/10.1007/s11060-010-0476-2.

      [10] J. C. L. Alfonso et al., "The biology and mathematical modelling of glioma invasion: A review", J. Roy. Soc. Interface, vol. 14, no. 136, pp. 1-20, 2017. https://doi.org/10.1098/rsif.2017.0490.

      [11] H. M. Byrne, T. Alarcon, M. R. Owen, S. D. Webb, P. K. Maini, "Modelling aspects of cancer dynamics: A review", Philos. Trans. Roy. Soc. London A Math. Phys. Sci., vol. 364, no. 1843, pp. 1563-1578, 2006.

      [12] H. L. P. Harpold, E. C. Alvord, K. R. Swanson, "The evolution of mathematical modeling of glioma proliferation and invasion", J. Neuropathol. Experim. Neurol., vol. 66, no. 1, pp. 1-9, 2007. https://doi.org/10.1097/nen.0b013e31802d9000.

      [13] H. Hatzikirou, A. Deutsch, C. Schaller, M. Simon, K. Swanson, "Mathematical modelling of glioblastoma tumour development: A review", Math. Models Methods Appl. Sci., vol. 15, no. 11, pp. 1779-1794, 2005. https://doi.org/10.1142/S0218202505000960.

      [14] A. H. Juffer, U. Marin, O. Niemitalo, J. Koivukangas, "Computer modeling of brain tumor growth", Mini Rev. Med. Chem., vol. 8, no. 14, pp. 1494-1506, 2008. https://doi.org/10.2174/138955708786786471.

      [15] Y. Kam, K. A. Rejniak, A. R. A. Anderson, " Cellular modeling of cancer invasion: Integration of in silico and in vitro approaches ", J. Cellular Physiol., vol. 227, no. 2, pp. 431-438, 2012. https://doi.org/10.1002/jcp.22766.

      [16] N. L. Martirosyan, E. M. Rutter, W. L. Ramey, E. J. Kostelich, Y. Kuang, M. C. Preul, "Mathematically modeling the biological properties of gliomas: A review", Math. Biosci. Eng., vol. 12, no. 4, pp. 879-905, 2015. https://doi.org/10.3934/mbe.2015.12.879.

      [17] N. Meghdadi, M. Soltani, H. Niroomand-Oscuii, F. Ghalichi, "Image based modeling of tumor growth", Australas. Phys. Eng. Sci. Med., vol. 39, no. 3, pp. 601-613, 2016. https://doi.org/10.1007/s13246-016-0475-5.

      [18] A. Roniotis, K. Marias, V. Sakkalis, M. Zervakis, "Diffusive modelling of glioma evolution: A review", J. Biomed. Sci. Eng., vol. 3, pp. 501-508, May 2010. https://doi.org/10.4236/jbise.2010.35070.

      [19] S. Sanga, H. B. Frieboes, X. Zheng, R. Gatenby, E. L. Bearer, V. Cristini, " Predictive oncology: A review of multidisciplinary multiscale in silico modeling linking phenotype morphology and growth ", NeuroImage, vol. 37, pp. S120-S134, May 2007. https://doi.org/10.1016/j.neuroimage.2007.05.043.

      [20] Z. Wang, J. D. Butner, R. Kerketta, V. Cristini, T. S. Deisboeck, "Simulating cancer growth with multiscale agent-based modeling", Seminars Cancer Biol., vol. 30, pp. 70-78, Feb. 2015. https://doi.org/10.1016/j.semcancer.2014.04.001.

      [21] L. B. Edelman, J. A. Eddy, N. D. Price, " In silico models of cancer ", Wiley Interdiscipl. Rev. Syst. Biol. Med., vol. 2, no. 4, pp. 438-459, 2010. https://doi.org/10.1002/wsbm.75.

      [22] P. M. Altrock, L. L. Liu, F. Michor, "The mathematics of cancer: Integrating quantitative models", Nature Rev. Cancer, vol. 15, pp. 730-745, Nov. 2015. https://doi.org/10.1038/nrc4029.

      [23] E. Konukoglu, Modeling glioma growth and personalizing growth models in medical images, Nice, France:Univ. Nice Sophia Antipolis, 2009.

      [24] Z. Wang, T. S. Deisboeck, "Computational modeling of brain tumors: Discrete continuum or hybrid?" in Scientific Modeling and Simulations, Dordrecht, The Netherlands:Springer, vol. 15, pp. 381-393, 2008.

      [25] M. A. J. Chaplain, "Avascular growth angiogenesis and vascular growth in solid tumours: The mathematical modelling of the stages of tumour development", Math. Comput. Model., vol. 23, no. 6, pp. 47-87, 1996. https://doi.org/10.1016/0895-7177(96)00019-2.

      [26] T. Roose, S. J. Chapman, P. K. Maini, "Mathematical models of avascular tumor growth", SIAM Rev., vol. 49, no. 2, pp. 179-208, 2007. https://doi.org/10.1137/S0036144504446291.

      [27] R. P. Araujo, D. L. S. McElwain, "A history of the study of solid tumour growth: The contribution of mathematical modelling", Bull. Math. Biol., vol. 66, no. 5, pp. 1039-1091, 2004. https://doi.org/10.1016/j.bulm.2003.11.002.

      [28] C. S. Hogea, B. T. Murray, J. A. Sethian, "Simulating complex tumor dynamics from avascular to vascular growth using a general level-set method", J. Math. Biol., vol. 53, no. 1, pp. 86-134, 2006. https://doi.org/10.1007/s00285-006-0378-2.

      [29] J. D. Humphrey, K. R. Rajagopal, "A constrained mixture model for growth and remodeling of soft tissues", Math. Models Methods Appl. Sci., vol. 12, no. 3, pp. 407-430, 2002. https://doi.org/10.1142/S0218202502001714.


 

View

Download

Article ID: 17930
 
DOI: 10.14419/ijet.v7i4.17930




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.