Carotid wall segmentation in longitudinal ultrasound images using structured random forest

Y. Nagaraj, C. S. Asha, Hema Sai Teja A., A. V. Narasimhadhan

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


Edge detection is a primary image processing technique used for object detection, data extraction, and image segmentation. Recently, edge-based segmentation using structured classifiers has been receiving increasing attention. The intima media thickness (IMT) of the common carotid artery is mainly used as a primitive indicator for the development of cardiovascular disease. For efficient measurement of the IMT, we propose a fast edge-detection technique based on a structured random forest classifier. The accuracy of IMT measurement is degraded owing to the speckle noise found in carotid ultrasound images. To address this issue, we propose the use of a state-of-the-art denoising method to reduce the speckle noise, followed by an enhancement technique to increase the contrast. Furthermore, we present a novel approach for an automatic region of interest extraction in which a pre-trained structured random forest classifier algorithm is applied for quantifying the IMT. The proposed method exhibits IMTmean ± standard deviation of 0.66mm ± 0.14, which is closer to the ground truth value 0.67mm ± 0.15 as compared to the state-of-the-art techniques.

Original languageEnglish
Pages (from-to)753-767
Number of pages15
JournalComputers and Electrical Engineering
Publication statusPublished - 07-2018

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • General Computer Science
  • Electrical and Electronic Engineering


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