Yamanakkanavar, Y.Asha, C.S.Teja A, H.S.Narasimhadhan, A.V.2026-02-052018Computers and Electrical Engineering, 2018, 69, , pp. 753-767457906https://doi.org/10.1016/j.compeleceng.2018.02.010https://idr.nitk.ac.in/handle/123456789/25105Edge 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 IMT<inf>mean</inf> ± 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. © 2018 Elsevier LtdCardiologyDecision treesDiseasesEdge detectionExtractionObject detectionSpeckleUltrasonic imagingCardio-vascular diseaseCommon carotid arteryGamma correctionIntima-media thicknessRandom forestsUltrasound imagingWiener filteringImage segmentation