An automated detection method for the MCA dot sign of acute stroke in unenhanced CT
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.
KeywordsComputed 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.
- 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
- 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
- 16.Vapnik VN. The nature of statistical learning theory: statistics for engineering and information science. 2nd ed. New York: Springer-Verlag; 1999.Google Scholar
- 18.Johnson RA, Wicherm DW. Applied multivariate statistical analysis. Englewood Cliffs: Prentice Hall; 2007.Google Scholar
- 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.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