Skip to main content

Automatic Detection of Brain Strokes in CT Images Using Soft Computing Techniques

Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB,volume 25)

Abstract

Stroke is the cerebrovascular issue influencing blood supply to the mind that predominantly influences individuals over 65 years old. This article proposes an automatic technique to perceive and orchestrate the sorts of strokes starting with 2D cerebrum CT images. The methodology is divided into four steps. In the introductory step, preprocessing may be performed on the image to expel unwanted disturbance by applying median filtering. In second step, different texture-based features are extricated utilizing wavelet packet transform (WPT) for classification. In the following step, Linear Discriminant Analysis (LDA) is utilized to diminish the dimensionality of the features. Finally, the diminished group of feature is connected to the supervised learning techniques for classification of normal and infected region. The goal of the proposed work is to build up a framework that accurately extracts the stroke region from CT images that helps doctors in their diagnosis decisions. The performance of the proposed scheme has fundamentally enhanced the stroke classification precision contrasted with other neural system-based classifier.

Keywords

  • Computed tomography (CT)
  • Wavelet packet transform (WPT)
  • Linear discriminate analysis (LDA)
  • Classification

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-61316-1_5
  • Chapter length: 25 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   129.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-61316-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   169.99
Price excludes VAT (USA)
Hardcover Book
USD   169.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig.  21

References

  1. The Atlas of Heart Disease and Stroke. World Health Organization, 2004

    Google Scholar 

  2. Ministry of Public Health (2002) Burden of disease and injuries in Thailand Priority setting for policy, pp A14–A16

    Google Scholar 

  3. Jalivand M, Li X, Zwick T, Wiesbeck W, Pancera, E (2011) Hemorrhagic stroke detection via UWB medical imaging. In: Antennas and propagation (EUCAP), proceeding of the 5th European conference on, pp 2911–2914, April (2011)

    Google Scholar 

  4. Neethu S, Venkataraman D (2015) Stroke detection in brain using CT images. J Artif Intell Evol Algorithms Eng Syst 324:379–386 (Springer, 2015)

    Google Scholar 

  5. Roy S, Chatterjee K, Bandyopadhyay SK (2014) Segmentation of acute brain stroke from MRI of brain image using power law transformation with accuracy estimation. J Adv Comput, Netw Inf 27, 453–461 (Springer International Publishing)

    Google Scholar 

  6. Wysoki MG et al (1998) Head trauma: CT scan interpretation by radiology residents versus Staff Radiologists. Radiology 208(1):8–125

    CrossRef  Google Scholar 

  7. Erickson BJ, Bartholmai B (2002) Computer aided detection and diagnosis at the start of the third millennium. J Digit Imag 15(2):59–68

    CrossRef  Google Scholar 

  8. Sharma N, Aggarwal LM (2010) Automated medical image segmentation techniques. J Med Phys, Assoc Med Physicists India 3, 35(1)

    Google Scholar 

  9. Vymazal J, Rulseh AM, Keller J, Janouskova L (2012) Comparison of CT and MR imaging in Ischemic Stroke. In: Insight into imaging 3(6), 619–627

    Google Scholar 

  10. Praveen R Mirajakar, Arun Vikas Singh, Dr. Kishan Asok Bhagwat, Ashalatha M E.: Acute ischemic stroke detection using wavelet based fusion of CT and MRI images. In: International conference on advances in computing, communication and informatics, IEEE (2015)

    Google Scholar 

  11. Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 31(4–5):198–211

    CrossRef  Google Scholar 

  12. Yoshida H, Nappi J (2001) Three dimensional computer-aided diagnosis scheme for detection of colonic polyps. IEEE Trans Med Imaging 20(12):1261–1274

    CrossRef  Google Scholar 

  13. Yoshida H, Nappi J, MacEneaney P, Rubin DT, Dachman AH (2000) Computer aided diagnosis scheme for detection of polyps at CT colonography. Radiographics 22(4):963–979

    CrossRef  Google Scholar 

  14. Schlachetzki F, Herzberg M, Hlscher T, Ertl M, Zimmermann M, Ittner KP et al (2012) Transcranial ultrasound from diagnosis to early stroke treatment: part 2: prehospital neurosonography in patients with acute stroke: the Regensburg stoke mobile project. Cerebrovasc Dis 33:262–271

    CrossRef  Google Scholar 

  15. Perez N, Valdes J,Guevara M, Silva A (2009) Spontaneous intracerebral hemorrhage image analysis methods: a survey. In: Advances in computational vision and medical image processing (2009)

    Google Scholar 

  16. Liu Y, Rothfus WE, Kanade T (1997) Content based 3D neuoradiologic image retrieval: preliminary results. In: IEEE content-based video and image retrieval workshop associated with CVPR97

    Google Scholar 

  17. Dhawan AP, Loncaric S, Hitt K, Broderick J, Brott T (1993) Image analysis and 3D visualization of intracerebral brain hemorrhage. In: IEEE Symposium on Computer Based Medical Systems, pp 140–145

    Google Scholar 

  18. Cosic D, Loncaric S (1997) Computer system for quantitative analysis of ischemic from ct head images. In: 19th Annual international conference of the IEEE (1997)

    Google Scholar 

  19. Chan T (2007) Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Compute Med Imaging Graph 31(4), 285–298

    Google Scholar 

  20. Zhang WL, Wang XZ (2007) Feature extraction and classification for human brain CT images. Proc IEEE Int Conf Mach Learn Cybern 2:19–22

    Google Scholar 

  21. Fallahi AR, Pooyan M, Khotanlou H A new approach for classification of human brain CT images based on morphological operations. J Biomed Sci Eng 3, 78–82

    Google Scholar 

  22. Chawla M, Sharma S, Sivaswamy J, Kishore L (2009) A method for automatic detection and classification of stroke from brain CT images. In: Proceedings of the Annual International conference IEEE Engineering in Medicine and Biology Society (EMBC 09)

    Google Scholar 

  23. Liu R, Tan CL, Leong TY, Lee CK, Pang BC, Lim CT, Tian Q, Tang S, Zhang Z (2008) Hemorrhage slices detection in brain ct images. In: International conference on pattern recognition, pp 1–4

    Google Scholar 

  24. Hara T, Matoba N, Zhou X, Yokoi S, Aizawa H, Fujita H, Sakashita K, Matsuoka T (2007) Automated detection of extradural and subdural hematoma for content-enhanced CT images in emergency medical care. In: Proceeding of SPIE (2007)

    Google Scholar 

  25. Das DS, Rani GU, Moorthy GS (2012) Analysis of PSNR for Different 3D DWT. J Int J Inf Technol Secur 1, ISSN 2279–008X

    Google Scholar 

  26. Przelaskowski A et al (2007) Improved early stroke detection: wavelet based perception enhancement of computerized tomography exams. J Comput Biol Med 37, pp 524–533, Science Direct

    Google Scholar 

  27. Seemann T (2012) Digital image processing using local segmentation book. Monash University, Australia

    Google Scholar 

  28. Badioze Zaman H et al (2009) Automated segmentation and retrieval system for CT head images. In: IVIC 2009. LNCS, 5857, pp 97–109. Springer, Berlin

    Google Scholar 

  29. Rajini NH, Bhavani R (2013) Computer aided detection of ischemic stroke using segmentation and texture features. J Meas 46, 1865–1874 (Science Direct)

    Google Scholar 

  30. Nagalkar V, Agrawal S (2012) Ischemic stroke detection using digital image processing by fuzzy methods. J Int J Latest Res Sci Technol 1(4), 345–347

    Google Scholar 

  31. Balasooriya U, Perera MUS (2012) Intelligent brain hemorrhage diagnosis using artificial neural networks. In: IEEE business, engineering & industrial applications colloquium (BEIAC)

    Google Scholar 

  32. http://funnotes.net/tofpages/TopicOfFortnight.php?tofTpcFl=topicoffortnight2

  33. Lee TH (2009) Segmentation of CT brain images using unsupervised clustering. J Vis 12(2), 131–138

    Google Scholar 

  34. Ramos OE, Rezaei B Scene segmentation and interpretation image segmentation using region growing. M.Sc. thesis, Computer

    Google Scholar 

  35. Gonzalez CR, Woods ER (2000) Digital image processing, 2nd edn. Prentice Hall, New Jersey

    Google Scholar 

  36. Kaganami HG, Beij Z (2009) Region based detection versus edge detection. In: IEEE transaction on intelligent information hiding and multimedia signal processing, pp 1217–1221

    Google Scholar 

  37. Kyaw MM (2013) Computer aided detection system for hemorrhage contained region. J Int J Comput Sci Inf Technol 1(1)

    Google Scholar 

  38. Pauline J, Hitesh Sh (2012) Brain tumor classification using wavelet and texture based neural network. J Int J Sci Eng Res 3(10). ISSN 2229–5518

    Google Scholar 

  39. Coifman R, Meyer Y, Quake S, Wickerhauser MV (1993) Signal processing and compression with wavelet packets. In: Progress in wavelet analysis and applications (Toulouse, 1992), pp 77–93, Frontiers, Gif

    Google Scholar 

  40. Licciardi G, Pacifici F, Tuia D, Prasad S, West T, Giacco F, Thiel C, Inglada J, Christophe E, Chanussot J, Gamba P (2009) Decision fusion for the classification of hyper spectral data: outcome of the 2008 GRS-S data fusion contest. In: IEEE transaction on geo science and remote sensing, vol 47, no 11, pp 3857–3865

    Google Scholar 

  41. Arivazhagan S, Ganesan L (2003) Texture segmentation using wavelet transform, Elsevier. Pattern Recog Lett 24:3197–3203

    CrossRef  MATH  Google Scholar 

  42. Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst, Man Cybern 3(6):610–621

    CrossRef  Google Scholar 

  43. Hema Rajini N, Bhavani R (2013) Automatic classification of computed tomography brain images using ANN, k-NN and SVM. J. AI & Society 29, 97–102 (Springer)

    Google Scholar 

  44. Sundararaj GK, Balamurugan V (2014) Robust classification of primary brain tumor in computer tomography images using K-NN and linear SVM. In: International conference on contemporary and informatics

    Google Scholar 

  45. McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5

    Google Scholar 

  46. Cristianini N, Shawe T, Taylor J (2000) An introduction to support vector machines and other Kernel-based learning methods, 1st edn. Cambidge University Press, New York

    Google Scholar 

  47. Suykens JAK, Vandewalle J (1999) Least squares support vector machines classifiers . Neural Process Lett 9(3), 293–300

    Google Scholar 

  48. Olesen J, Gustavsson A, Svensson M, Wittchen HU, Jonsson B (2012) CDBE2010 study group: European brain council the economic cost of brain disorders in Europe. J Eur J Neurol 19:155–162

    CrossRef  Google Scholar 

  49. Padma Nanthagopal A, Sukanesh Rajamony R (2012) Automatic classification of brain computed tomography images using wavelet based statistical texture features. J Vis Soc Jpn 1–10

    Google Scholar 

  50. Amato F, López A, Peña-Méndez EM, Vaňhara P, Hampl A, Havel J (2013) Artificial neural network in medical diagnosis. J Appl Biomed 111, 47–58

    Google Scholar 

  51. irtskhulava L, Wong J, Al-Majeed S, Pearce G (2015) Artificial neural network model in stroke diagnosis. In: UKSIM-AMSS International conference on modeling and simulation

    Google Scholar 

  52. Li GZ, Yang J, Liu GP, Xue L (2004) Feature selection for multi-class problems using support vector machines. Image Process Pattern Recogn 109–111

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to B. S. Maya .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Maya, B.S., Asha, T. (2018). Automatic Detection of Brain Strokes in CT Images Using Soft Computing Techniques. In: Hemanth, J., Balas , V. (eds) Biologically Rationalized Computing Techniques For Image Processing Applications. Lecture Notes in Computational Vision and Biomechanics, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-61316-1_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-61316-1_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61315-4

  • Online ISBN: 978-3-319-61316-1

  • eBook Packages: EngineeringEngineering (R0)