Radiological Physics and Technology

, Volume 7, Issue 1, pp 79–88 | Cite as

An automated detection method for the MCA dot sign of acute stroke in unenhanced CT

  • Noriyuki Takahashi
  • Yongbum Lee
  • Du-Yih Tsai
  • Eri Matsuyama
  • Toshibumi Kinoshita
  • Kiyoshi Ishii


The hyperdense middle cerebral artery (MCA) dot sign representing a thromboembolus is one of the important computed tomography (CT) findings for acute stroke on unenhanced CT images. Our purpose in this study was to develop an automated method for detection of the MCA dot sign of acute stroke on unenhanced CT images. The algorithm of the method which we developed consisted of 5 major steps: extraction of the sylvian fissure region, initial identification of MCA dots based on the morphologic top-hat transformation, feature extraction of candidates, elimination of false positives (FPs) by use of a rule-based scheme, and classification of candidates using a support vector machine (SVM) classifier with four features. Our database comprised 297 CT images obtained from seven patients with the MCA dot sign. The performance of this scheme for classification of the MCA dot sign was evaluated by means of a leave-one-case out method. The performance of the classification by use of the SVM achieved a maximum sensitivity of 97.5 % (39/40) at a FP rate of 1.28 per image. The sensitivity for detection of the MCA dot sign was 97.5 % (39/40) with a FP rate of 0.5 per hemisphere. The method we developed has the potential to detect the MCA dot sign of acute stroke on unenhanced CT images.


Computed tomography Acute stroke MCA dot sign Morphological top-hat transformation 


Conflict of interest

None of the authors have any conflicts of interest associated with this study.


  1. 1.
    Adams H, Adams R, del Zoppo G. Guidelines for the early management of patients with ischemic stroke: 2005 guidelines update a scientific statement from the Stroke Council of the American Heart Association/American Stroke Association. Stroke. 2005;36:916–23.PubMedCrossRefGoogle Scholar
  2. 2.
    Adams HP Jr, Adams RJ, Brott T, del Zoppo GJ, Furlan A, Goldstein LB, Grubb RL, Higashida R, Kidwell C, Kwiatkowski TG, Marler JR, Hademenos GJ. Guidelines for the early management of patients with ischemic stroke: a scientific statement from the Stroke Council of the American Stroke Association. Stroke. 2003;34:1056–83.PubMedCrossRefGoogle Scholar
  3. 3.
    Balbel PA, Demchuk AM, Zhang J, Buchan AM. Validity and reliability of a quantitative computed tomography score in predicting outcome of hyperacute stroke before thrombolytic therapy. Lancet. 2000;355:1670–4.CrossRefGoogle Scholar
  4. 4.
    Provenzale JM, Jahan R, Naidich TP, Fox AJ. Assessment of the patient with hyperacute stroke: imaging and therapy. Radiology. 2003;229:347–59.PubMedCrossRefGoogle Scholar
  5. 5.
    Schriger DL, Karafut M, Starkman S, Krueger M, Saver JL. Cranial computed tomography interpretation in acute stroke: physician accuracy in determining eligibility for thrombolytic therapy. JAMA. 1998;279:1293–7.PubMedCrossRefGoogle Scholar
  6. 6.
    Wardlaw JM, Mielke O. Early signs of brain infarction at CT: observer reliability and outcome after thrombolytic treatment—systematic review. Radiology. 2005;235:444–53.PubMedCrossRefGoogle Scholar
  7. 7.
    Leys D, Pruvo JP, Godefroy O, Rondepierre P, Leclerc X. Prevalence and significance of hyperdense middle cerebral artery in acute stroke. Stroke. 1992;23:317–24.PubMedCrossRefGoogle Scholar
  8. 8.
    Manelfe C, Larrue V, von Kummer R, Bozzao L, Ringleb P, Bastianello S. Association of hyperdense middle cerebral artery sign with clinical outcome in patients treated with tissue plasminogen activator. Stroke. 1999;30:769–72.PubMedCrossRefGoogle Scholar
  9. 9.
    Kirchhof K, Welzel T, Mecke C, Zoubaa S, Sartor K. Differentiation of white, mixed, and red thrombi: value of CT in estimation of the prognosis of thrombolysis—phantom study. Radiology. 2003;228:126–30.PubMedCrossRefGoogle Scholar
  10. 10.
    Barber PA, Demchuk AM, Hudon ME, Pexman JH, Hill MD, Buchan AM. Hyperdense sylvian fissure MCA “dot” sign : a CT marker of acute ischemia. Stroke. 2001;32:84–8.PubMedCrossRefGoogle Scholar
  11. 11.
    Leary MC, Kidwell CS, Villablanca JP, Starkman S, Jahan R, Duckwiler GR, Gobin YP, Sykes S, Gough KJ, Ferguson K, Llanes JN, Masamed R, Tremwel M, Ovbiagele B, Vespa PM, Vinuela F, Saver JL. Validation of computed tomographic middle cerebral artery “dot” sign: an angiographic correlation study. Stroke. 2003;34:2636–40.PubMedCrossRefGoogle Scholar
  12. 12.
    Shetty SK, The MCA. Dot sign. Radiology. 2006;241:315–8.PubMedCrossRefGoogle Scholar
  13. 13.
    Somford DM, Nederkoorn PJ, Rutgers DR, Kappelle LJ, Mali WP, van der Grond J. Proximal and distal hyperattenuating middle cerebral artery signs at CT: different prognostic implications. Radiology. 2002;223:667–71.PubMedCrossRefGoogle Scholar
  14. 14.
    Maldjian JA, Chalela J, Kasner SE, Liebeskind D, Detre JA. Automated CT segmentation and analysis for acute middle cerebral artery stroke. Am J Neuroradiol. 2001;22:1050–5.PubMedGoogle Scholar
  15. 15.
    Takahashi N, Lee Y, Tsai DY, Kinoshita T, Ouchi N, Ishii K. Computer-aided detection scheme for identification of hypoattenuation of acute stroke in unenhanced CT. Radiol Phys Technol. 2012;5:98–104.PubMedCrossRefGoogle Scholar
  16. 16.
    Vapnik VN. The nature of statistical learning theory: statistics for engineering and information science. 2nd ed. New York: Springer-Verlag; 1999.Google Scholar
  17. 17.
    Cristianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods. Cambridge: Cambridge University Press; 2000.CrossRefGoogle Scholar
  18. 18.
    Johnson RA, Wicherm DW. Applied multivariate statistical analysis. Englewood Cliffs: Prentice Hall; 2007.Google Scholar
  19. 19.
    Kim EY, Lee SK, Kim DJ, Suh SH, Kim J, Heo JH, Kim DI. Detection of thrombus in acute ischemic stroke: value of thin-section noncontrast-computed tomography. Stroke. 2005;36:2745–7.PubMedCrossRefGoogle Scholar
  20. 20.
    Camargo ECS, Furie KL, Singhal AB, Roccatagliata L, Cunnane ME, Halpern EF, Harris GJ, Smith WS, Gonzalez RG, Koroshetz WJ, Lev MH. Acute brain infarct: detection and delineation with ct angiographic source images versus nonenhanced CT scans. Radiology. 2007;244:541–8.PubMedCrossRefGoogle Scholar
  21. 21.
    Lin K, Rapalino O, Law M, Babb JS, Siller KA, Pramanik BK. Accuracy of the Alberta stroke program early CT score during the first 3 hours of middle cerebral artery stroke: comparison of noncontrast CT, CT angiography source images, and CT perfusion. Am J Neuroradiol. 2008;29:931–6.PubMedCrossRefGoogle Scholar
  22. 22.
    Nezu T, Koga M, Nakagawara J, Shiokawa Y, Yamagami H, Furui E, Kimura K, Hasegawa Y, Okada Y, Okuda S, Kario K, Naganuma M, Maeda K, Minematsu K, Toyoda K. Early ischemic change on CT versus diffusion-weighted imaging for patients with stroke receiving intravenous recombinant tissue-type plasminogen activator therapy: stroke acute management with urgent risk-factor assessment and improvement (SAMURAI) rt-PA registry. Stroke. 2011;42:2196–200.PubMedCrossRefGoogle Scholar
  23. 23.
    Gadda D. A case of bilateral dense middle cerebral arteries with CT angiographic confirmation of vascular occlusion. Emerg Radiol. 2003;10:142–3.PubMedCrossRefGoogle Scholar

Copyright information

© Japanese Society of Radiological Technology and Japan Society of Medical Physics 2013

Authors and Affiliations

  • Noriyuki Takahashi
    • 1
  • Yongbum Lee
    • 2
  • Du-Yih Tsai
    • 2
  • Eri Matsuyama
    • 3
  • Toshibumi Kinoshita
    • 4
  • Kiyoshi Ishii
    • 1
  1. 1.Department of RadiologySendai City HospitalSendaiJapan
  2. 2.Department of Radiological Technology, Graduate School of Health SciencesNiigata UniversityNiigataJapan
  3. 3.Department of RadiologyNishi Sapporo HospitalSapporoJapan
  4. 4.Department of RadiologyResearch Institute for Brain and Blood Vessels-AkitaAkitaJapan

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