Skip to content
KPI Science News
ISSN 2617-5509 · e‑ISSN 2663-7472 Open Access · CC BY 4.0
Vol. · Issue 4 · 2019 Oct 15, 2019 article

ANALYSIS OF EFFECTIVENESS OF THRESHOLDING IN PERFUSION ROI DETECTION ON T2-WEIGHTED MR IMAGES WITH ABNORMAL BRAIN ANATOMY

SA
Svitlana M. Alkhimova Corresponding Igor Sikorsky Kyiv Polytechnic Institute, Ukraine Ukraine
SS
Svitlana V. Sliusar Igor Sikorsky Kyiv Polytechnic Institute, Ukraine Ukraine
PublishedOct 15, 2019
PDF (Ukrainian)
KPISN 4 VOL · 4
VOL · ISS 4 · 2019 View issue

Abstract

Background. The brain perfusion ROI detection being a preliminary step, designed to exclude non-brain tissues from analyzed DSC perfusion MR images. Its accuracy is considered as the key factor for delivering correct results of perfusion data analysis. Despite the large variety of algorithms developed on brain tissues segmentation, there is no one that works reliably and robustly on T2-weighted MR images of a human head with abnormal brain anatomy. Therefore, thresholding method is still the state-of-the-art technique that is widely used as a way of managing pixels involved in brain perfusion ROI in modern software applications for perfusion data analysis. Objective. This paper presents the analysis of effectiveness of thresholding techniques in brain perfusion ROI detection on T2-weighted MR images of a human head with abnormal brain anatomy. Methods. Four threshold-based algorithms implementation are considered: according to Otsu method as global thresholding, according to Niblack method as local thresholding, thresholding in approximate anatomical brain location, and brute force thresholding. The result of all algorithms is images with pixels’ values changed to zero for background regions (air pixels and pixels that represent non-brain tissues) and original values for foreground regions (brain perfusion ROIs). The analysis is done using comparison of qualitative perfusion maps produced from thresholded images and from the reference ones (manual brain tissues delineation by experienced radiologists). The same DSC perfusion MR datasets of a human head with abnormal brain anatomy from 12 patients with cerebrovascular disease are used for comparison. Results. Pearson correlation analysis showed strong positive ( r was ranged from 0.7123 to 0.8518, p < 0.01) and weak positive ( r < 0.35, p < 0.01) relationship in case of conducted experiments with CBF, CBV, MTT and Tmax perfusion maps, respectively. Linear regression analysis showed at level of 95 % confidence interval that perfusion maps produced from thresholded images were subject to scale and offset errors in all conducted experiments. Conclusions. The experimental results showed that widely used thresholding methods are an ineffective way of managing pixels involved in brain perfusion ROI. Thresholding as brain segmentation tool can lead to poor placement of perfusion ROI and, as a result, produced perfusion maps will be subject to artifacts and can cause falsely high or falsely low perfusion parameter assessment.

Keywords

#Perfusion dynamic susceptibility contrast magnetic resonance imaging #Abnormal brain scans #Region of interest #Segmentation #Thresholding

References

  1. B. Lanzman and J.J. Heit, “Advanced MRI measures of cerebral perfusion and their clinical applications”, Topics in Magnetic Resonance Imaging , vol. 26, no. 2, pp. 83–90, 2017. doi: 10.1097/RMR.0000000000000120
  2. K. Welker et al. , “ASFNR recommendations for clinical performance of MR dynamic susceptibility contrast perfusion imaging of the brain”, Am. J. Neuroradiol. , vol. 36, no. 6, pp. E41–E51, 2015. doi: 10.3174/ajnr.A4341
  3. G.-H. Jahng et al. , “Perfusion magnetic resonance imaging: A comprehensive update on principles and techniques”, Korean J. Radiol. , vol. 15, no. 5, pp. 554–577, 2014. doi: 10.3348/kjr.2014.15.5.554
  4. Galinovic et al. , “Automated VS manual delineations of regions of interest- a comparison in commercially available perfusion MRI software”, BMC Medical Imaging , vol. 12, no. 16, pp. 1–3, 2012. doi: 10.1186/1471-2342-12-16
  5. S.M. Alkhimova and O.S. Zheleznyi, “Automatizations problem for region of interest detection in perfusion magnetic resonance imaging”, in Proc. Modern Directions of Theoretical and Applied Researches ‘2015 , Odesa, 2015, vol. 1, no. 4, pp. 90–93.
  6. S.M. Alkhimova, “Detection of perfusion ROI as a quality control in perfusion analysis”, in Proc. Science, Research, Development. Technics and Technology , Berlin, 2018, pp. 57–59.
  7. D.W. Shattuck et al. , “Magnetic resonance image tissue classification using a partial volume model”, NeuroImage , vol. 13, no. 5, pp. 856–876, 2001. doi: 10.1006/nimg.2000.0730
  8. B.D. Ward, “3dIntracranial: automatic segmentation of intracranial region”, Biophysics Research Institute, Medical College of Wisconsin, UK, Tech. Rep., June 1999.
  9. P. Kalavathi and V.S. Prasath, “Methods on skull stripping of MRI head scan images – A review”, J. Digit. Imaging , vol. 29, pp. 365–379, 2016. doi: 10.1007/s10278-015-9847-8
  10. S. Datta and P.A. Narayana, “Automated brain extraction from T2‐weighted magnetic resonance images”, J. Magnetic Resonance Imaging , vol. 33, no. 4, pp. 822–829, 2011. doi: 10.1002/jmri.22510
  11. M. Jenkinson et al. , “BET2: MR-based estimation of brain, skull and scalp surfaces”, in Proc. 12th Annual Meeting of the Organization for Human Brain Mapping , 2005, vol. 17, p. 167.
  12. S. Rajagopalan et al. , “Robust fast automatic skull stripping of MRI-T2 data”, Proc. SPIE , vol. 5747, pp. 485–495, 2005. doi: 10.1117/12.594651
  13. Despotović et al. , “MRI segmentation of the human brain: challenges, methods, and applications”, Computational and Mathematical Methods in Medicine , vol. 2015, pp. 1–23, 2015. doi: 10.1155/2015/450341
  14. D. Selvaraj and R. Dhanasekaran, “MRI brain image segmentation techniques – A review”, Indian J. Comp. Sci. Eng. , vol. 4, no. 5, pp. 364–381, 2013.
  15. S. Tripathi et al. , “A review of brain MR image segmentation techniques”, in Proc. Int. Conf. Recent Innovations in Applied Science, Engineering & Technology (AET-2018) , Mumbai, India, June 16–17, 2018, pp. 62–69.
  16. N. Otsu, “A threshold selection method from gray-level histograms”, IEEE Trans. Syst. Man Cybern. , vol. 9, no. 1, pp. 62–66, 1979. doi: 10.1109/TSMC.1979.4310076
  17. W. Niblack, An Introduction to Digital Image Processing . Englewood Cliffs, NJ: Prentice-Hall International, 1986.
  18. N. Senthilkumaran and C. Kirubakaran, “Efficient implementation of Niblack thresholding for MRI brain image segmentation”, Int. J. Comp. Sci. Inform. Technol. , vol. 5, no. 2, pp. 2173–2176, 2014.
  19. S.M. Alkhimova, “Automated detection of regions of interest for brain perfusion MR images”, KPI Sci. News , no. 5, pp. 14–21, 2018. doi: 10.20535/1810-0546.2018.5.146185
  20. K. Somasundaram and T. Kalaiselvi, “A method for filling holes in objects of medical images using region labeling and run length encoding schemes”, in Proc. National Conference on Image Processing (NCIMP) , 2010, pp. 110–115.
  21. W. Burger and M.J. Burge, Principles of Digital Image Processing – Core Algorithms . London, UK: Springer-Verlag, 2009. doi: 10.1007/978-1-84800-195-4
§ 06 — Related

Similar articles in this journal

Related peer-reviewed studies published in this journal.
View all issues

AN APPROACH TO CONSTRUCTING AN INFORMATIVE FEATURE SPACE OF TECHNICAL INDICATORS FOR FINANCIAL PROCESSES BASED ON NEURAL NETWORK MODELS

Roman Tertychnyi, Denys Chuprin, Petro Bidyuk

Background. Financial time series are characterized by nonlinearity, non-stationarity, and a high level of noise, which significantly complicates their modeling and…

#informative feature space construction approach #technical indicators #Parkinson volatility #neural networks
10.20535/kpisn.2026.3.359610

Similar Articles

1-10 of 174

You may also start an advanced similarity search for this article.