CFP last date
20 December 2024
Reseach Article

Intelligent Medical Decision System for Identifying Ultrasound Carotid Artery Images with Vascular Disease

by N.Santhiyakumari, M. Madheswaran
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 13
Year of Publication: 2010
Authors: N.Santhiyakumari, M. Madheswaran
10.5120/285-447

N.Santhiyakumari, M. Madheswaran . Intelligent Medical Decision System for Identifying Ultrasound Carotid Artery Images with Vascular Disease. International Journal of Computer Applications. 1, 13 ( February 2010), 32-39. DOI=10.5120/285-447

@article{ 10.5120/285-447,
author = { N.Santhiyakumari, M. Madheswaran },
title = { Intelligent Medical Decision System for Identifying Ultrasound Carotid Artery Images with Vascular Disease },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 13 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 32-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number13/285-447/ },
doi = { 10.5120/285-447 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:46:27.025951+05:30
%A N.Santhiyakumari
%A M. Madheswaran
%T Intelligent Medical Decision System for Identifying Ultrasound Carotid Artery Images with Vascular Disease
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 13
%P 32-39
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The objective of this work is to develop and implement an intelligent medical decision system for identifying Ultrasound (US) carotid artery images with vascular diseases. The proposed method categorizes the carotid artery subjects into normal and diseased subjects’ namely cerebrovascular and cardiovascular diseases. For each and every preprocessed ultrasound carotid artery image, contours are extracted using contour extraction techniques. Multilayer Back Propagation Network (MBPN) system has been developed for categorizing the carotid artery subjects. The obtained results show that MBPN system provides higher classification efficiency, with minimum training and testing time. It helps in developing Medical Decision System (MDS) for ultrasound carotid artery images. It can also be used as secondary observer in clinical decision making.

References
  1. Troccaz, J., Baumann, M., Berkelman, P., Cinquin, P., Daanen, V., Leroy, A., Marchal, M., Payan, Y., Promayon, E., Voros, S., Bart, S., Bolla, M., Chartier- Kastler, E., Descotes, J.-L., Dusserre, A., Giraud, J.-Y., Long, J.-A., Moalic, R., Mozer, P., 2006. Medical image computing and computer-aided medical interventions applied to soft tissues: Work in progress in urology. Proc. IEEE 94(9):1665– 1677.
  2. Summers, R. M., 2003. Road maps for advancement of radiologic computer- aided detection in the 21st century. Radiology 229: 11– 13.
  3. Maryellen, L., Giger, N. K., Armato, S. G., 2001. Computer-aided diagnosis in medical imaging. IEEE Trans. Med. Imag. 20 (12):1205–1208.
  4. Doi, K., 2005. Current status and future potential of computer-aided diagnosis in medical imaging. Brit. J. Radiol. 78:S3–S19.
  5. Erickson, B. J., Bartholmai, B., 2002. Computer-aided detection and diagnosis at the start of the third millennium. J. Digit. Imaging 15:59–68.
  6. Bommanna Raja, K., Madheswaran, M., Thyagarajah, K., 2008. A Hybrid Fuzzy- Neural system for Computer –Aided Diagnosis of Ultrasound Kidney Images Using Prominent Features. J. Med. Syst. 32: 65–83.
  7. Aoyama, M., Li, Q., Katsuragawa, S., MacMahon, H., Doi, K., 2002. Automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules on chest images. Med. Phys. 29(5):701–708.
  8. Arimura, H., Katsuragawa, S., Suzuki, K., Li, F., Shiraishi, J., Sone, S., Doi, K., 2004. Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening. Acad. Radiol. 11(6):617–629.
  9. McCulloch, C. C., Kaucic, R. A., Mendonça, P. R. S., Walter, D. J., Avila, R. S., 2004. Model-based detection of lung nodules in computed tomography exams: thoracic computer-aided diagnosis. Acad. Radiol. 11(3):258–266.
  10. Gletsos, M., Mougiakakou, S. G., Matsopoulos, G. K., Nikita, K. S., Nikita, A. S., Kelekis, D., 2003. A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier. IEEE Trans. Inf. Technol. Biomed. 7 (3):153–162.
  11. Verikas, A., Gelzinis, A., Bacauskiene, M., Uloza, V., 2006.Towards a computer aided diagnosis system for vocal cord diseases. Artif. Intell. Med. 36:71– 84.
  12. Yoshida, H., Dachman, A. H., 2005. CAD techniques, challenges and controversies in CT colonography. J. Abdom. Imaging 30:24–39.
  13. Jegelevicius D. and Lukosevicius A. 2002.Ultrasonic measurements of human carotid artery wall intima-media Thickness. Ultragarsas. 43-47.
  14. Pierre-Jean Touboul. Clinical impact of intima media measurement. European Journal of Ultrasound. 16.105 -113, 2002.
  15. Huang, S.-F., Chang, R.-F., Chen, D.-R., Moon, W. K., 2004. Charac-terization of speculation on ultrasound lesions. IEEE Trans. Med. Imag. 23(1):111– 121.
  16. Loizou, C. P., Christodoulou, C., Pattischis, C. S., Istepanian, R. H., Pantziaris, M., Nicolaides, A., 2002. Speckle reduction in ultrasound images of atherosclerotic carotid plaque. IEEE Proc. 14th Intl. Conf. Digital Signal Processing. Santorini, Greece, 1:525–528.
  17. Santhiyakumari N., Madheswaran M. 2008. Medical decision making system using intima media thickness measurement. Proceedings of Fifth International Conference on Medical Informatics and Telemedicine Conference.1.
  18. Santhiyakumari N., Madheswaran M. 2006. Estimation of layer thickness of arterio carotis using Dynamic Programmin Procedure. Proceedings of third Cairo International Biomedical Engineering Conference. IP2- 4. 1- 4.
  19. Santhiyakumari N., Madheswaran M. 2008. Non-Invasive Evaluation of carotid artery wall thickness using improved dynamic programming technique. Journal of Signal, Image and Video processing (Springer). 2. 183-193.
  20. Santhiyakumari N., Madheswaran M. 2007. Extraction of Intima- Media Layer of Arteria- Carotis and Evaluation of its thickness using Active contour approach. Proceedings of International Conference on intelligent and advanced systems. IP_MS1. 582-586.
  21. Santhiyakumari N., Madheswaran M. 2008. Analysis of Atherosclerosis for identification of Cerebrovascular and Cardiovascular Diseases using Active Contour Segmentation of Carotid Artery. Proceedings of International Symposium on Global Trends in Bio Medical Informatics Research, Education and Commercialization. 1. 40.
  22. Santhiyakumari N., Madheswaran M. 2009. Analysis of atherosclerosis for identification of cerebrovascular and cardiovascular diseases using active contour segmentation of carotid artery’, International Journal of Biomedical Engineering and consumer health Informatics. 1(2). 121-125.
  23. Priddy L.K. and Keller E.P. 2005. Artificial neural networks an Introduction. SPIE Press. Bellingham. Washington.
  24. Haykin S. 1994. Neural networks. Macmillan College Publishing Company. Englewood Cliffs. NJ.
  25. Karunanithi N., Grenney W.J., Whitley D. and Bovee K. 1994. Neural Networks for River Flow Prediction. J. Computing in Civil Eng. ASCE. 8(2). 201-220.
Index Terms

Computer Science
Information Sciences

Keywords

US carotid artery image Contour extraction Multilayer back propagation network Neural network classifier Medical decision system