Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A local search approximation algorithm for k-means clustering
Computational Geometry: Theory and Applications - Special issue on the 18th annual symposium on computational geometry—SoCG2002
Automatic 3d segmentation of intravascular ultrasound images using region and contour information
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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
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In this paper a novel method that automatically detects the lumenintima border on an intravascular ultrasound sequence (IVUS) is presented. First, a 3D co-occurrence matrix was used to efficiently extract the texture information of the IVUS images through the temporal sequence. By extracting several co-occurrence matrices a complete characterization feature space was determined. Secondly, using a k-means algorithm, all the pixels in the IVUS images were classified by determining if they belong to either the lumen or the other vessel tissues. This enables automatic clustering and therefore no learning step was required. The classification of the pixels within the feature space was obtained using 3 clusters: two clusters for the vessel tissues, one cluster for the lumen, while the remaining pixels are labeled as unclassified. Experimental results show that the proposed method is robust to noisy images and yields segmented lumen-intima contours validated by an expert in more than 80% of a total of 300 IVUS images.