Automatic Segmentation and Classification of Brain Hemorrhage Regions Using Multi-class Support Vector Machine in Computed Tomography (CT-Scan) Images

Document Type : Original Article (s)

Authors

1 MSc Student, Department of Telecommunications, School of Electrical Engineering, Islamic Azad University, Najafabad Branch, Isfahan, Iran

2 Assistant Professor, Department of Telecommunications, School of Electrical Engineering, Islamic Azad University, Najafabad Branch, Isfahan, Iran

Abstract

Background: Brain hemorrhage due to head trauma is one of the most common causes of death. Early diagnosis of location and type of brain hemorrhage is crucial. For saving the patients completely, it is necessary to detect the correct location and type of the hemorrhage in an early stage. In this study, we introduced an automatic brain hemorrhage detection and classification algorithm to improve and accelerate the process of physicians’ decision-making.Methods: To achieve the purpose, at first a segmentation algorithm was usedto detect and separate the hemorrhage regions from other parts of the brain. Then, a number of appropriate features from each detected hemorrhage region were extracted and then by using genetic algorithm, the most convenient features were selected. The utilized computed tomography (CT-scan) images in this research were collected from Kashani hospital CT-scan center (Isfahan, Iran) and were of 70 men and women between the ages of 15-60 years.Findings: Using the proposed segmentation and classification algorithm, the segmentation accuracy for different types of hemorrhages [epidural (EDH), intracerebral (ICH) and subdural (SDH)] were obtained as 96.87%, 96.10%, 92.15%, respectively. Also, intraventricular hemorrhage (IVH) was detected and separated from other types of hemorrhage with the accuracy rate of 91.82% and classified with accuracy rate of 94.13%.Conclusion: In this research, an independent and automatic brain hemorrhage detection and classification algorithm was assessed. Our proposed algorithm is an attempt to improve and accelerate the process of physicians’ decision-making to save the patients’ lives. By using the proposed algorithm, we were able to detect and classify four kinds of dangerous hemorrhages in CT-scan images and accelerat the process of the victim’s treatment.

Keywords

References

  1. Hu Q, Qian G, Aziz A, Nowinski W. Segmentation of brain from computed tomography head images. Proceedings of the 27th Annual International Conference of the IEEE-MBS Engineering in Medicine and Biology; 2006 Jan 17-18; Shanghai, China. p. 3375-8.
  2. Lauric A. Soft image segmentation of CT brain data [Thesis]. Medford, MA: Tufts University 2007.
  3. Loncaric S, Cosic D, Dhawan AP. Hierarchical segmentation of CT head images. Proceedings of the 18th Annual International Conference of the IEEE-MBS Engineering in Medicine and Biology; 1996 31 Oct-3 Nov; Amsterdam, Netherlands. p. 736-7.
  4. Majcenic Z, Loncaric S. CT image labeling using simulated annealing algorithm [Online]. [cited 1998]. Available from: URL: https://bib.irb.hr/datoteka/49284.eus98st.pdf. p. 2513-6.
  5. Maksimovic R, Stankovic S, Milovanovic D. Computed tomography image analyzer: 3D reconstruction and segmentation applying active contour models--'snakes'. Int J Med Inform 2000; 58-59: 29-37.
  6. Mancas M, Gosselin B. Towards an automatic tumor segmentation using iterative watersheds. Proceedings of the SPIE 2004; 5370: 1598-608.
  7. Zaki MD, Fauzi FA, Besar R. Abnormalities detection in serial computed tomography brain images using multi-level segmentation approach. Multimedia Tools and Applications 2011; 54(2): 231-40.
  8. Ramteke R, Khachane MY. Automatic medical image classification and abnormality detection using K-Nearest Neighbour. International Journal of Advanced Computer Research 2012; 2(6): 190.
  9. Kharrat A, Gasmi K, Ben Messaoud M, Benamrane N, Abid M. A hybrid approach for automatic classification of brain MRI using genetic algorithm and support vector machine. Leonardo Journal of Sciences 2010; (17): 71-82.
  10. Chowdhury DR, Chatterjee M, Samanta RK. An artificial neural network model for neonatal disease diagnosis. Journal of Artificial Intelligence and Expert Systems 2011; 2(3): 96-106.
  11. Gong T, Liu R, Tan CL, Farzad N, Lee CK, Pang BC, et al. Classification of CT brain images of head trauma. Proceedings of the 2nd IAPR International Conference on Pattern Recognition in Bioinformatics; 2007 Oct 1; Berlin, Germany. p. 401-8.
  12. Sharma B, Venugopalan K. Automatic segmentation of brain CT scan image to identify hemorrhages. International Journal of Computer Applications 2010; 40(10): 1-4.
  13. Osher S, Sethian JA. Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics 1988; 79(1): 12-49.
  14. Li C, Xu C, Member S, Gui C, Fox MD. Distance Regularized Level Set Evolution and Its Application to Image Segmentation. IEEE Transactions on Image Processing 2010; 19(12): 3243-54.
  15. Maduskar P, Acharyya M. Automatic identification of intracranial hemorrhage in non-contrast CT with large slice thickness for trauma cases. Proceedings of SPIE 2009; 7260.
  16. Yang M, Kpalma K, Ronsin J. A survey of shap e feature extraction techniques [Online]. [cited 2008 Jul 15]; Available from: URL:
  17. http://hal.archives-ouvertes.fr/docs/00/44/60/37/PDF/ARS-Journal-SurveyPatternRecognition.pdf. p. 43-90.
  18. Shahangian B, Pourghassem H. Automatic brain hemorrhage segmentation and classification in CT scan images. Proceedings of the 8th Conference on Machine Vision and Image; 2013 Sep 10-12; Zanjan, Iran.
  19. Hsu CW, Lin C. A comparison of methods for multiclass support vector machines. IEEE Computational Intelligence Society 2002; 13(2): 415-25.
Journal of Isfahan Medical School
Volume 32, Issue 284 - Serial Number 284
March and April 2014
Pages 631-646

Files

History

  • Receive Date: 18 December 2013
  • Revise Date: 01 November 2024
  • Accept Date: 06 April 2022

Share

How to cite

Statistics