Intravascular Ultrasound Tissue Characterization with Sub-class Error-Correcting Output Codes
Journal of Signal Processing Systems
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
A new method for characterization of coronary plaque composition via IVUS images
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Error-Correcting Ouput Codes Library
The Journal of Machine Learning Research
In-Vivo IVUS tissue classification: a comparison between RF signal analysis and reconstructed images
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
IEEE Transactions on Information Technology in Biomedicine
Shape-Driven Segmentation of the Arterial Wall in Intravascular Ultrasound Images
IEEE Transactions on Information Technology in Biomedicine
Texture information in run-length matrices
IEEE Transactions on Image Processing
Hi-index | 0.00 |
Virtual Histology-Intravascular Ultrasound (VH-IVUS) is widely used for studying atherosclerosis plaque composition. However, one of the main limitations of the VH-IVUS relates to its dependence to the Electrocardiogram (ECG)-gated acquisition. To overcome this limitation, this paper proposes a robust image-based approach for characterization of the plaques using IVUS images. The proposed method consists of three main steps of (1) shadow detection: as an efficient preprocessing step to identify and remove acoustic shadow regions; (2) feature extraction: a combination of gray-scale based features and textural descriptors; and (3) classification: to classify each pixel into one of the three classes (calcium, necrotic core and fibro-fatty). In order to evaluate the efficiency of the proposed algorithm two in-vivo and ex-vivo data sets are considered. The kappa values of 0.639 on in-vivo and 0.628 on ex-vivo tests with VH-IVUS and the histology images labeled by the experts respectively indicate the effectiveness of the proposed algorithm.