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US Characterization and Identification of Symptomatic Carotid Plaques

US Characterization and Identification of Symptomatic Carotid Plaques. J. Seabra, J. Sanches Instituto de Sistemas e Robótica Instituto Superior Técnico Portugal. L. Pedro, J. Fernandes Instituto Cardiovascular de Lisboa Hospital de Santa Maria Portugal.

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US Characterization and Identification of Symptomatic Carotid Plaques

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  1. US Characterization and Identification of Symptomatic Carotid Plaques J. Seabra, J. Sanches Instituto de Sistemas e Robótica Instituto Superior Técnico Portugal L. Pedro, J. Fernandes Instituto Cardiovascular de Lisboa Hospital de Santa Maria Portugal 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

  2. Overview • Introduction • Objectives • Methods • Data management • Image processing • Feature extraction • Classification • Experimental results • Conclusions and Future work

  3. Introduction • Symptomatic Plaques are lesions which have produced neurological symptoms associated with cerebral events such as stroke, TIA and AF. • The degree of stenosis is a major indicator of stroke; however, several trials (NASCET, ECST) report that not all carotid plaques showing significant stenosis ( > 70%) are harmful. • Surgical treatment carries non-negligible risks and reduced cost-effectiveness.

  4. Introduction • Parameters of plaque structure and grey-scale image appearance have shown to be associated with neurological symptoms [El-Barghouty98, Elatrozy98]. • Related work on the topic of plaque characterization using: subjective visual analysis [Wilhjelm98], a combination of structure parameters and histogram features [Pedro02], 1st and 2nd order statistics [Christodolou03], a combination of texture features and classifiers [Mongiakakou07] and power spectrum analysis on multilevel image decomposition [Kyriacou09]. • The importance of speckle modeling for tissue characterization has been reported [Thijssen03]. • A recently proposed algorithm [Seabra_EMBC08, Seabra_ISBI10] is able to split the ultrasound image in speckle-free and speckle components: • Speckle-free image  echo-morphology content • Speckle field  texture information

  5. Introduction Echolucent region Necrotic Core Lipidic tissue and Fibrotic cap Homogeneous, hyperechogenic

  6. Objectives • Objectively characterize the symptomatic plaque using conventional B-Mode Ultrasound (inexpensive, non-invasive and practical method). • Investigate the relative significance of the features used in the classification framework for identifying symptoms in carotid plaques.

  7. Methods DATA MANAGEMENT • 146 bifurcation plaques from 99 patients, 102 asymptomatic and 44 symptomatic. Average age of patients: 68 years old (41-88). • Patients observed consecutively through neurological consultation which included non-invasive ultrasound examination. • A plaque was considered symptomatic when neurological symptoms were observed in the previous 6 months.

  8. Methods II. FEATURE EXTRACTION I. IMAGE PROCESSING III. CLASSIFICATION

  9. Methods I. IMAGE PROCESSING • Normalization to a 0-190 grey-scale, envelope image estimation and de-speckling. Noiseless B-mode envelope RF Speckle Normalized

  10. Methods II. FEATURE EXTRACTION • Morphology parameters: evidence of plaque disruption, presence of fibrous cap, degree of stenosis, plaque echo-structure appearance (homog. vs. heterog.). • Histogram parameters: mean, median, variance, energy, entropy, percentiles. • Rayleigh mixture models: applied to the envelope image, models the echogenic content [Seabra_ISBI10]. • Rayleigh parameters: theoretical estimators computed from the de-speckled image. • Texture features: extracted from grey-level co-occurrence matrices (GLCM), wavelet decomposition and autoregressive models.

  11. Methods RMM Histogram features Texture features Rayleigh theoretical estimators II. FEATURE EXTRACTION Morphology + Image-based parameters = 114 echographic parameters used for classification

  12. Methods III. CLASSIFICATION • Features were trained with Adaptive Boosting (AdaBoost) [Schapire01] which aims at building a strong classifier from a set of weak (1- feature decision) classifiers. • 5 different classifiers were trained, using distinct feature sets: • F.1: clinical • F.2: clinical + histogram • F.4: all features except clinical • F.5: all features • F.3: all features, but considering only one image source  investigate usefulness of information collected from speckle-free and speckle components

  13. Experimental Results Only “image” features used It is preferable to extract texture from speckle and intensity from noiseless image • Classifier performance assessed with Leave-One-Patient-Out (LOPO) cross-validation technique. • A classifier with high diagnostic power would detect the maximum no. TP and lowest no. FN while providing the lowest no. FP.

  14. Experimental Results • To demonstrate the suitability of AdaBoost, we compare this method with the Activity Index based classifier [Pedro02] according to the parameters used in the latter (Morphology, GSM, P40)

  15. Experimental Results • AdaBoost provides an automatic selection of the most discriminant features used for training (discriminating between symptomatic and asymptomatic plaques).

  16. Experimental Results • The most relevant parameters are related to stenosis degree and, interestingly, to texture parameters extracted from speckle.

  17. Conclusions • A plaque classification framework for studying and identifying the symptomatic plaque has been developed, fusing clinical information with intensity and texture parameters. • This method uses a set of features extracted from different image sources, namely the normalized, envelope, noiseless and speckle images. • A classifier based on such features outperforms the classifiers based on single feature sources (such as clinical or image-based) up to 99.2% accuracy and 100% sensitivity. • Feature analysis highlights the importance of image-based features, particularly those computed from speckle, for an accurate description of the symptomatic plaque.

  18. Future work • What? Having design a robust classifier and identified the most relevant parameters that positively correlate with symptoms we are currently developing a method for predicting the occurrence of symptoms. • How? Through a Risk Prediction Score that combines conditional probabilities using the aforementioned a priori information. • Where? This method is being applied on ultrasound images acquired in the beginning of a 4-year follow-up study in 112 asymptomatic plaques (13 plaques developed symptoms).

  19. ACKNOWLEDGMENTS BIOSONDA – Comércio de Material Hospitalar, Lda FCT SFRH/BD/37633/2007 Portuguese Government – FCT (ISR/IST plurianual funding) Thank you!Questions? 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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