Automatic detection of the existence of subarachnoid hemorrhage from clinical ct images

Journal of Medical Systems

Volumn 36, Issue 3, 2012, Pages 1259-1270

  Li, Yonghong ^a,b,c,d   Wu, Jianhuang ^a,b   Li, Hongwei ^e   Li, Degang ^f   Du, Xiaohua ^a,b   Chen, Zhijun ^a,b   Jia, Fucang ^a,b   Hu, Qingmao ^a,b,g  

^a SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

^b CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^c INSTITUTE OF COMPUTING TECHNOLOGY   (China)

^d UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^e NINGXIA MEDICAL UNIVERSITY   (China)

^f INNER MONGOLIA MEDICAL COLLEGE   (China)

^g University Town of Shenzhen   (China)

Author keywords

Atlas based registration; Non contrast CT; Subarachnoid hemorrhage; Support vector machine

Indexed keywords

ADULT; AGED; ARTICLE; COMPUTED TOMOGRAPHY SCANNER; COMPUTER ASSISTED TOMOGRAPHY; CONTROLLED STUDY; FEMALE; HUMAN; MAJOR CLINICAL STUDY; MALE; SENSITIVITY AND SPECIFICITY; SUBARACHNOID HEMORRHAGE; SUBARACHNOID SPACE;

ADULT; AGED; ALGORITHMS; CASE-CONTROL STUDIES; CHINA; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MALE; MIDDLE AGED; RETROSPECTIVE STUDIES; SENSITIVITY AND SPECIFICITY; SUBARACHNOID HEMORRHAGE; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84864043512     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-010-9587-8     Document Type: Article

References (22)

1. Petrea, R. E., Beiser, A. S., Seshadri, S., Kelly-Hayes,M., Kase, C. S., andWolf, P. A., Gender differences in stroke incidence and poststroke disability in the framingham heart study. Stroke 40:1032-1037, 2009.
2. Van, G. J., Kerr, R. S., and Rinkel, G. J. E., Subarachnoid haemorrhage. Lancet 369:306-318, 2007. (Pubitemid 46136491)
3. Schievink, W. I., Intracranial aneurysms. N Engl J. Med. 336:28-40, 1997. (Pubitemid 26428285)
4. Edlow, J. A., Malek, A. M., and Ogilvy, C. S., Aneurysmal subarachnoid hemorrhage: Update for emergency physicians. J. Emerg. Med. 34:237-251, 2008.
5. Schwartz, D. T., Sensitivity of computed tomography for subarachnoid hemorrhage. Ann. Emerg. Med. 53:160-161, 2009.
6. Chan, T., Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Comput. Med. Imaging Graph. 31(4-5):285-298, 2007. (Pubitemid 46601783)
7. Yuh, E. L., Gean, A. D., Manley, G. T., Callen, A. L., and Wintermark, M., Computer-aided assessment of head computed tomography (CT) studies in patients with suspected traumatic brain injury. J. Neurotrauma 25:1163-1172, 2008.
8. Loncaric, S., Dhawan, A. P., Broderick, J., and Thomas, B., 3-D image analysis of intra-cerebral brain hemorrhage from digitized CT films. Comput. Meth. Programs Biomed. 46(3):207-216, 1995.
9. Bardera, A., Boada, I., Feixasa, M., Remollob, S., Blascob, G., Silvab, Y., and Pedrazabet, S., Semi-automated method for brain hematoma and edema quantification using computed tomography. Comput. Med. Imaging Graph. 33:304-311, 2009.
10. Baldy, R. E., Brindle, G. S., Ewusi-Mensah, I., Jacobson, R., Reveley, M. A., Turner, S. W., and Lishman, W. A., A fullyautomated computer-assisted method of CT brain scan analysis for the measurement of cerebrospinal fluid spaces and brain absorption density. Neuroradiology 28(2):109-117, 1986. (Pubitemid 16111823)
11. Li, Y. H., Jia, F. C., and Hu, Q. M., Several practical problems in clinical head CT image registration. The 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE), 2010.
12. Boulfelfel, D., Rangayyan, R. M., Hahn, L. J., Kloiber, R., and Kuduvalli, G. R., Two-dimensional restoration of single photon emission computed tomography images using the kalman filter. IEEE Trans. Med. Imaging 13(1):102-109, 1994.
13. Toprak, A., and Güler, Ì., Angiograph image restoration with the use of rule base fuzzy 2D Kalman filter. Expert Syst. Appl. 35:1752-1761, 2008.
14. Hu, Q. M., Qian, G. Y., Aziz, A., and Nowinski, W. L., Segmentation of brain from computed tomography head images. Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 3375-3378, 2005. (Pubitemid 46229650)
15. Mazziotta, J. C., Toga, A. W., Evans, A., Fox, P., and Lancaster, J., A probabilistic atlas of the human brain: theory and rationale for its development. The International Consortium for Brain Mapping (ICBM). Neuroimage 2:89-101, 1995.
16. Ibanez, L., Schroeder, W., Ng, L., and Cates, J., The ITK software guide. Kitware Inc, Clifton Park, 2003.
17. Skilling, J., and Bryan, R. K., Maximum entropy image reconstruction: General algorithm. Mon. Not. R. Astron. Soc. 211:111-124, 1984.
18. Cortes, C., and Vapnik, V., Support-vector network. Mach. Learn. 273-297, 1995.
19. Vapnik, V., Statistical learning theory, 2nd edition. Wiley, New York, 1998.
20. Boser, B. E., Guyon, I. M., and Vapnik, V., A training algorithm for optimal margin classifiers. In Fifth Annual Workshop on Computational Learning Theory. ACM, Pittsburgh, 1992.
21. Chang, C.-C., and Lin, C.-J., LIBSVM : a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/-cjlin/libsvm.
22. Duda, R. O., Hart, P. E., and Stork, D. G., Pattern classification. John Wiley & Sons, New York, pp. 483-484, 2001.