1. Rayleigh Mixture Model and its Application for Ultrasound-based Plaque Characterization José Seabra, Francesco Ciompi, Oriol Pujol, Petia Radeva and João Sanches Instituto de Sistemas e Robótica, IST Lisboa Centre de Visió per Computador, Barcelona Workshop Programa Doutoral em Engenharia Biomédica 15 Julho 2009

2. Introduction Rayleigh Mixture ModelPlaque ClassificationResultsConclusions  Vulnerable plaques are a major source of carotid and coronary circulatory events  B-mode ultrasound and IVUS provide accurate representation of the arterial wall and plaque  Identification of vulnerable plaques comes from the correct modeling of tissue echo- morphology and characterization of its composition  Under particular conditions, pixel intensity observations belonging to ultrasound images are well modeled by Rayleigh probability density functions (pdfs)  Proposal:  to characterize the echo-morphology of plaques by use of a mixture of Rayleigh distributions  to incorporate the Rayleigh Mixture Model (RMM) in a 3-type plaque classification problem

3. Introduction Rayleigh Mixture Model Plaque ClassificationResultsConclusions  A plaque (as other tissue) can be regarded as a complex structure (see Fig.1) where phenomena, including absorption, diffuse and structural scattering, occur and combine Figure 1. Tissue Acoustic model

4. Introduction Rayleigh Mixture Model Plaque ClassificationResultsConclusions  Figure 2. Effect of the Rayleigh reflectivity parameter on the pdf

5. Introduction Rayleigh Mixture Model Plaque ClassificationResultsConclusions  Simulation study for testing the RMM in a synthetic image Figure 3. a) Tissue sample and b) diagonal D intensity profile. c) MLE of the Rayleigh pdf for region S, and d) comparison between MLE Rayleigh pdf and mixture pdf for the whole tissue sample

6. IntroductionRayleigh Mixture Model Plaque Classification ResultsConclusions Figure 4. a) IVUS data acquisition and analysis from a post-mortem human coronary artery. B) Histological analysis of a slice of the artery. (c) a reliable correspondence in the IVUS image is established by using a suitable labeling software. (d) Rotation catheter, (e) Polar vs reconstructed IVUS image (d) (e)  Plaque characterization is based on an IVUS study of the coronary arteries  Features are based on images reconstructed from the RF data  67 plaques were labeled according to their composition as lipidic, fibrotic or calcified

7. IntroductionRayleigh Mixture Model Plaque Classification ResultsConclusions (a) (b) (c) (d) (e) (f) Figure 5. a) IVUS image showing three plaques (tissues) labeled according to their composition. (b-c) De-speckle and speckle image (the regularization effect is visible). (d-e) RMM estimated from the three labeled distinct plaques  RMM estimation for 3 different plaques, generation of de-speckled and speckle images

8. IntroductionRayleigh Mixture ModelPlaque Classification Results Conclusions Figure 6. a) Feature space, where the dataset of 67 plaques of different types is plotted according to the mixture coefficients. b) 3-type plaque-content characterization using RMM computed with different number of mixture components  Performance was evaluated by means of the Leave-One-Patient-Out (LOPO) cross-validation technique, using the Adaboost classifier with Error-Correcting-Output Codes (ECOC)  1st Result: Plaque-content classification using RMM features (b) (a)

9. IntroductionRayleigh Mixture ModelPlaque Classification Results Conclusions Figure 7. a) Feature space, where the dataset of 67 plaques of different types is plotted according to the mixture coefficients. b) 3-type plaque-content characterization using RMM computed with different number of mixture components. c) Graphical classification  2nd Result: Local-wise classification by use of a feature set including RMM, Speckle, Textural and Spectral features RMM Speckle Texture Spectrum Features Weights (b) (a) (c)

10. IntroductionRayleigh Mixture ModelPlaque ClassificationResults Conclusions  A generic method to model the tissue echo-morphology is proposed based on the mixture of Rayleigh distributions  Our study suggests that different plaque types can be distinguished based on the coefficients (weights) and Rayleigh parameters of each distribution of the mixture  The inclusion of mixture parameters in a classification framework has shown to improve the discriminative power between different plaque types, leading to high classification accuracies  A medical supervised plaque classification tool based on RMM can be developed, given that what is suspected to be a plaque is previously segmented and provided to the algorithm  FUTURE:  Change from Rayleigh mixture to Rician mixture  Apply this “mixture concept” and its features to classification of symptomatic carotid plaques