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Abstract Vulnerable plaques are the commonest source of cardiovascular problems.

A Rayleigh mixture approach for modeling ultrasound plaque morphology. J. C. Seabra and J. Miguel Sanches Institute for Systems and Robotics / Instituto Superior Técnico Lisboa, Portugal. Experimental Results

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Abstract Vulnerable plaques are the commonest source of cardiovascular problems.

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  1. A Rayleigh mixture approach for modeling ultrasound plaque morphology J. C. Seabra and J. Miguel Sanches Institute for Systems and Robotics / Instituto Superior Técnico Lisboa, Portugal • Experimental Results • RMM adequacy for modeling different plaque types is tested on real, validated, data set of 67 IVUS plaques (24 fibrotic, 12 lipidic, 31 calcified). • The RMM is applied to the entire set of pixels enclosed in each plaque. The Single Rayleigh Model (SRM) is used for comparison. • Data set was trained with different features (median, SRM and RMM) using Adaptive Boosting. Testing was made with Leave One Patient Out (LOPO) cross-validation. • Abstract • Vulnerable plaques are the commonest source of cardiovascular problems. • Plaque morphology can help the identification of such lesions. • This work introduces a robust yet simple strategy – Rayleigh Mixture Model (RMM)– to describe complex textural patterns in ultrasound images. • The application of RMM in an IVUS dataset enables to distinguish different plaque components/types. • Problem Formulation • Pixel intensities in ultrasound images are considered random variables, described by the following mixture distribution: where σj is the parameter of the Rayleigh PDF: and are the mixture parameters to be estimated. • The Expectation Maximization algorithm is used to solve the following optimization problem: where the likelihood function is: • The solution* is given by: where is the distribution of the unobserved pixels. *cf: J. Seabra, J. Sanches, F. Ciompi, and P. Radeva. Ultrasonographic plaque characterization using a rayleigh mixture model. In Proceedings of IEEE ISBI, pages 1–4, Rotterdam, The Netherlands, Apr 2010. • Conclusions • The RMM algorithm enables to correctly identify different tissue types on IVUS images. • This method is useful for plaque characterization, with high classification scores being achieved. (jseabra@isr.ist.utl.pt) RecPad2010 - 16th edition of the Portuguese Conference on Pattern Recognition, UTAD University, Vila Real city, October 29th

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