Ultrasonic speckle formation, analysis and processing applied to tissue characterization
Pattern Recognition Letters - Speciqal issue: Ultrasonic image processing and analysis
Using output codes to boost multiclass learning problems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Ultrasound plaque enhanced activity index for predicting neurological symptoms
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
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A correct modelling of tissue morphology is determinant for the identification of vulnerable plaques. This paper aims at describing the plaque composition by means of a Rayleigh Mixture Model applied to ultrasonic data. The effectiveness of using a mixture of distributions is established through synthetic and real ultrasonic data samples. Furthermore, the proposed mixture model is used in a plaque classification problem in Intravascular Ultrasound (IVUS) images of coronary plaques. A classifier tested on a set of 67 in-vitro plaques, yields an overall accuracy of 86% and sensitivity of 92%,94% and 82%, for fibrotic, calcified and lipidic tissues, respectively. These results strongly suggest that different plaques types can be distinguished by means of the coefficients and Rayleigh parameters of the mixture distribution.