Optimal Feature Selection for Carotid Artery Image Segmentation Using Evolutionary Computation


Article Preview

Atherosclerosis is hardening of arteries due to high blood pressure and high cholesterol. It causes heart attacks, stroke and peripheral vascular disease and is the major cause of death. In this paper we have attempted a method to identify the presence of plaque in carotid artery from ultrasound images. The ultrasound image is segmented using improved spatial Fuzzy c means algorithm to identify the presence of plaque in carotid artery. Spatial wavelet, Hilbert Huang Transform (HHT), Moment of Gray Level Histogram (MGLH) and Gray Level Co-occurrence Matrix (GLCM) features are extracted from ultrasound images and the feature set is reduced using genetic search process. The intima media thickness is measured using the proposed method. The IMT values are measured from the segmented image and trained using MLBPNN neural network. The neural network classifies the images into normal and abnormal.



Edited by:

R. Edwin Raj, M. Marsaline Beno and M. Carolin Mabel




I. M. Farook et al., "Optimal Feature Selection for Carotid Artery Image Segmentation Using Evolutionary Computation", Applied Mechanics and Materials, Vol. 626, pp. 79-86, 2014

Online since:

August 2014




* - Corresponding Author

[1] Yongjian Yu and Scott T. Acton Speckle Reducing Anisotropic Diffusion, IEEE Transactions On Image Processing, Vol. 11, No. 11, November (2002).

DOI: https://doi.org/10.1109/tip.2002.804276

[2] H.S. Sheshadri, A. Kandaswamy, Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms, Computerized Medical Imaging and Graphics 31 (2007) 46–48.

DOI: https://doi.org/10.1016/j.compmedimag.2006.09.015

[3] R. Polikar, Fundamental Concepts & An Overview of the Wavelet Theory: Tutorial, (1996).

[4] M. Vasantha, V. Sunniah Bharathi, T. Dhamodharan, Medical image features, extraction, selection and classification, International Journal of Engineering Sciences and Technology 2 (2010) 2071–(2076).

[5] Norden E. Huang and Zhaohua Wu, A review on Hilbert-Huang Transform: Method and its Applications to Geophysical Studies.

DOI: https://doi.org/10.1029/2007rg000228

[6] G. Holmes, A. Donkin, I.H. Witten, WEKA: a machine learning workbench, in: Proceedings of the 1994 Second Australian and New Zealand Conference on Intelligent Information Systems, 1994, p.357–361.

DOI: https://doi.org/10.1109/anziis.1994.396988

[7] K.S. Chuang, H.L. Tzeng, S. Chen, J. Wu, T.J. Chen, Fuzzy c-means clustering with spatial information for image segmentation, Computerized Medical Imaging and Graphics: The Official Journal of the Computerized Medical Imaging Society 30 (2006).

DOI: https://doi.org/10.1016/j.compmedimag.2005.10.001

[8] N. Santhiyakumari, P. Rajendran, M. Madheswaran, Medical decision-making system of ultrasound carotid artery intima-media thickness using neural networks, Journal of Digital Imaging 24 (2011) 1112–1125.

DOI: https://doi.org/10.1007/s10278-010-9356-8

[9] Mehdi Hassana, Asmatullah Chaudhry, Asifullah Khana, Jin Young Kim, Carotid artery image segmentation using modified spatial fuzzy c-means and ensemble clustering, computer mothods and programs in biomedicine 108(2012) 1261-1276.

DOI: https://doi.org/10.1016/j.cmpb.2012.08.011

[10] Dhanalakshmi. S and Venkatesh. C, Classification of Ultrasound Carotid Artery Images using Texture Features, International review on Computers and Software, Vol 8, No 4, April (2013).