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
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.
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.
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.
RecPad th edition of the Portuguese Conference on Pattern Recognition, UTAD University, Vila Real city, October 29th