Identification of Carotid Asymptomatic Plaque Using Texture and Orientation Features for Health Care

D Sasikala,M Madheswaran

doi:10.19026/rjaset.8.940

Abstract

The carotid artery asymptomatic plaque identification has been done using the texture features at various orientation scales and presented in this study. The plaque region has been segmented using cubic spline interpolation method for multiresolution analysis using discrete wavelet transform. The features have been extracted using various scale and orientation of detail sub images using Gabor filters. It has been found from the analysis that the horizontal detail images have significantly greater values of the features than vertical detail images for symptomatic subjects. However, the horizontal detail images have shown low values compared to the vertical detail images for asymptomatic subjects. The algorithm is found to be simple and accurate for identifying the asymptomatic plaque clinically using less number of features.

Highlights

Atherosclerosis of the Carotid Artery (CA) is found to be an important risk factor in patients in the recent years
This study proposes Gabor transforms analysis of the detailed sub images of Discrete Wavelet Transform (DWT) for 5 scales and 7 orientations towards identifying the asymptomatic plaques of the carotid artery
The multiresolution analysis of plaques of asymptomatic and symptomatic plaque images shown in Fig. 3a and b is performed using Gabor transform under various scales over the decomposed sub images obtained from DWT

Summary

INTRODUCTION

Atherosclerosis of the Carotid Artery (CA) is found to be an important risk factor in patients in the recent years. Lambrou et al (2012) have evaluated the risk of stroke with confidence predictions based on ultrasound carotid image analysis and described the classification of plaque which was deposited in the inner wall of the carotid artery. This atherosclerosis disease was associated with the symptoms of stroke, Transient ischemic attack and amaurosis fugax. The characterization was based on many features extracted by Local Binary Pattern (LBP), Fuzzy Gray Level Co-occurrence Matrix (FGLCM) and Fuzzy Run Length Matrix (FRLM) These features along with degree of stenosis were used to train and test the classification of plaque by support vector machine. This study proposes Gabor transforms analysis of the detailed sub images of DWT for 5 scales and 7 orientations towards identifying the asymptomatic plaques of the carotid artery

MATERIALS AND METHODS

RESULTS AND DISCUSSION

CONCLUSION