Automatic Segmentation of Intima Media Complex in Carotid Ultrasound Images Using Support Vector Machine

• Published in: Arabian journal for science and engineering (2011) Vol. 44; no. 4; pp. 3489 - 3496
• Main Authors: Nagaraj, Y, Hema Sai Teja, A, Narasimhadhan, A V
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2019; Springer Nature B.V
• Subjects: Algorithms; Automation; Bayesian analysis; Biomarkers; Dynamic programming; Engineering; Heart diseases; Humanities and Social Sciences; Image segmentation; Medical imaging; multidisciplinary; Neural networks; Noise reduction; Quantitative analysis; Research Article - Computer Engineering and Computer Science; Science; Support vector machines; Ultrasonic imaging; Despeckle filtering; Ultrasound images; Carotid segmentation; Support vector machine; Intima media thickness
• ISSN: 2193-567X; 1319-8025; 2191-4281
• DOI: 10.1007/s13369-018-3549-8

The intima media thickness (IMT) of common carotid artery is a reliable measure of cardiovascular diseases. The quantification of IMT is the biomarker for clinical diagnosis of the risk of stroke. For robust measurement of IMT, the ultrasound carotid images must be free of speckle noise. To reduce the effect of speckle noise in the carotid ultrasound image, we propose to use Bayesian least square estimation filter. In addition, the enhancement step based on total variation- L 1 (TV- L 1 ) norm is performed to improve the robustness. Further more, we present a fully automated region of interest and segmentation of intima media complex based on support vector machine. The quantitative evaluation is carried out on 49 carotid ultrasound images. The proposed algorithm is compared with gradient-based methods like model based, dynamic programming, snake algorithm, and classifier-based segmentation using a neural network algorithm. The performance of the experimental result shows that the proposed method is robust in quantifying the IMT in carotid ultrasound images.