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Workshop Programa Doutoral em Engenharia Biomédica 15 Julho 2009. 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 - PowerPoint PPT Presentation
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Rayleigh Mixture Model and its Application for Rayleigh Mixture Model and its Application for Ultrasound-based Plaque CharacterizationUltrasound-based Plaque Characterization
José Seabra, Francesco Ciompi, Oriol Pujol, Petia Radeva and João Sanches
Instituto de Sistemas e Robótica, IST LisboaCentre de Visió per Computador, Barcelona
Workshop Programa Doutoral em Engenharia Biomédica
15 Julho 2009
Introduction Rayleigh Mixture Model Plaque Classification Results Conclusions
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
Introduction Rayleigh Mixture Model Plaque Classification Results Conclusions
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
Introduction Rayleigh Mixture Model Plaque Classification Results Conclusions
Figure 2. Effect of the Rayleigh reflectivity parameter on the pdf
Introduction Rayleigh Mixture Model Plaque Classification Results Conclusions
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
Introduction Rayleigh Mixture Model Plaque Classification Results Conclusions
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
Introduction Rayleigh Mixture Model Plaque Classification Results Conclusions
(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
Introduction Rayleigh Mixture Model Plaque 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)
Introduction Rayleigh Mixture Model Plaque 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)
Introduction Rayleigh Mixture Model Plaque Classification Results 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