Classification of Ultrasound Carotid Artery Images Using Texture Features
(*) Corresponding author
DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)
Abstract
The Cardio Vascular Disease (CVD) is the most fatal disease in the world in terms of deaths. The cardio vascular disease, associated with stroke and heart attack, is mainly caused by the increase in calcium deposition in the carotid artery. The Intima-Media Thickness (IMT) of the Common Carotid Artery (CCA) is widely used as an early indicator of CVD. The risk of CVD varies with age groups and this can be categorized based on the texture pattern of image of the carotid artery. This work presents an automated method to classify the carotid artery abnormalities by determining an appropriate Region of Interest (ROI), extracting the texture features, and calculating the IMT. The Ultrasound specimen image is acquired, intensity normalized, pre-processed to remove the speckle noise and then segmented. The texture analysis for segmented images is done using AM – FM techniques. The instantaneous values of the amplitude and frequency of each image specimen is obtained and it is quantized. It is then compared with the standard texture pattern, to identify whether the artery is normal or abnormal. Simulation results shows significant texture differences between the higher-risk age group of >60 years and the lower-risk age group of <50 and the 50-60 age group. Detecting the level of CVD was done by measuring the IMT. The overall process aims at implementing a fully automated system which helps in avoiding human errors, while measuring these values. The measurement technique is described in detail, highlighting the advantages compared to other methods and reporting the experimental results. Finally, the intrinsic accuracy of the system is estimated by an analytical approach. It also decreases inter-reader bias, potentially making it applicable for use in cardiovascular risk assessment.
Copyright © 2013 Praise Worthy Prize - All rights reserved.
Keywords
Full Text:
PDFReferences
C. P. Loizou et.al, “Multi-scale Amplitude Modulation-Frequency Modulation Texture Analysis of Ultrasound Images of the Intima and Media Layers of the Carotid Artery”, IEEE transactions on information technology in biomedicine, vol. 15, no. 2, march 2011.
Yan Xu, “A Modified Spatial Fuzzy Clustering Method Based on Texture Analysis for Ultrasound Image Segmentation” IEEE International Symposium on Industrial Electronics, July 5-8, 2009.
K.B.Jayanthi, R.S.D.Wahida Banu, 2009. “Carotid Artery Boundary Extraction Using Segmentation Techniques: A Comparative Study”, Sixth International Conference on Information Technology.
D.T. Kuan et.al “Adaptive restoration of images with speckle,” JEEE Trans. Acoustics, Speech and Sig. Proc., vol. ASSP-35, pp. 373-383, March 1987
T. Gustavsson, et.al., “Implementation and Comparison of Four Different Boundary Detection Algorithms for Quantitative Ultrasonic Measurements of the Human Carotid Artery”, IEEE Computers in Cardiology, vol. 24, 1997, pp. 69-72
Consolatina Liguori, Alfredo Paolillo, and Antonio Pietrosanto, (2001), “An Automatic Measurement System for the Evaluation of Carotid Intima-Media Thickness “, IEEE Ttransactions on Instrumentation and Measurement, vol. 50, no. 6.
D.T. Kuan and Alexander A. Sawchuk, T.C. Strand, Pierre Chavel, (1987), “Adaptive restoration of images with speckle”, IEEE Transactions on Acoustics, Speech, And Signal Processing, Vol. Assp-35, No. 3.
Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing Using Matlab”, Prentice Hall, 2003.
Silvia Delsanto, et.al.,(2005), “CULEX-Completely User-independent Layers Extraction: Ultrasonic Carotid Artery Images Segmentation”, Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China
T. Gustavsson, R. Abu-Gharbieh, G. Hamarneh, Q. Liang, (1997), “Implementation and Comparison of Four Different Boundary Detection Algorithms for Quantitative Ultrasonic Measurements of the Human Carotid Artery”, IEEE Computers in Cardiology Vol 24.
Weichu Xiao, Baolong Guo, Weihong Chen, Compatible Texture Image Segmentation Algorithm Based on Mean Shift Method, (2012) International Review on Computers and Software (IRECOS), 7 (4), pp. 1931-1937.
Alireza Samadi, Hossein Pourghassem, A Novel Age Estimation Algorithm based on Texture Features and Curvelet Transform in Human Face Images, (2012) International Review on Computers and Software (IRECOS), 7 (1), pp. 149-157.
Refbacks
- There are currently no refbacks.
Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize