Pattern Recognition
Automatic thresholding of gray-level pictures using two-dimensional entropy
Computer Vision, Graphics, and Image Processing
Handbook of pattern recognition & computer vision
Machine Learning
Digital Image Processing
Machine Learning
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Automatic thresholding for defect detection
Pattern Recognition Letters
Automatic IVUS segmentation of atherosclerotic plaque with stop & go snake
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Intravascular Imaging: Current Applications and Research Developments
Intravascular Imaging: Current Applications and Research Developments
Image analysis with local binary patterns
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
An automated method for lumen and media-adventitia border detection in a sequence of IVUS frames
IEEE Transactions on Information Technology in Biomedicine
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
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BACKGROUND: Intravascular ultrasound IVUS is an invasive imaging modality that provides high resolution cross-sectional images permitting detailed evaluation of the lumen, outer vessel wall and plaque morphology and evaluation of its composition. Over the last years several methodologies have been proposed which allow automated processing of the IVUS data and reliable segmentation of the regions of interest or characterization of the type of the plaque. OBJECTIVE: In this paper we present a novel methodology for the automated identification of different plaque components in grayscale IVUS images. METHODS: The proposed method is based on a hybrid approach that incorporates both image processing techniques and classification algorithms and allows classification of the plaque into three different categories: Hard Calcified, Hard-Non Calcified and Soft plaque. Annotations by two experts on 8 IVUS examinations were used to train and test our method. RESULTS: The combination of an automatic thresholding technique and active contours coupled with a Random Forest classifier provided reliable results with an overall classification accuracy of 86.14%. CONCLUSIONS: The proposed method can accurately detect the plaque using grayscale IVUS images and can be used to assess plaque composition for both clinical and research purposes.