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ATHEROMATIC?: IMAGING BASED SYMPTOMATIC CLASSIFICATION AND CARDIOVASCULAR STROKE INDEX ESTIMATION

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  • Title:
    ATHEROMATIC?: IMAGING BASED SYMPTOMATIC CLASSIFICATION AND CARDIOVASCULAR STROKE INDEX ESTIMATION
  • Author: Suri Jasjit S
  • Subjects: Human Necessities ; Medical OR Veterinary Science ; Hygiene ; Diagnosis ; Surgery ; Identification ; Physics ; Computing ; Calculating ; Counting ; Recognition Of Data ; Presentation Of Data ; Record Carriers ; Handling Record Carriers ; Image Data Processing OR Generation, In General ; Medicine ; Sciences ; Chemistry ; Physics
  • Language: English
  • Description: Characterization of carotid atherosclerosis and classification of plaque into symptomatic or asymptomatic along with the risk score estimation are key steps necessary for allowing the vascular surgeons to decide if the patient has to definitely undergo risky treatment procedures that are needed to unblock the stenosis. This application describes a statistical (a) Computer Aided Diagnostic (CAD) technique for symptomatic versus asymptomatic plaque automated classification of carotid ultrasound images and (b) presents a cardiovascular risk score computation. We demonstrate this for longitudinal Ultrasound, CT, MR modalities and extendable to 3D carotid Ultrasound. The on-line system consists of Atherosclerotic Wall Region estimation using AtheroEdge™ for longitudinal Ultrasound or Athero-CTView™ for CT or Athero-MRView from MR. This greyscale Wall Region is then fed to a feature extraction processor which uses the combination: (a) Higher Order Spectra; (b) Discrete Wavelet Transform (DWT); (c) Texture and (d) Wall Variability. Another combination uses: (a) Local Binary Pattern; (b) Law's Mask Energy and (c) Wall Variability. The output of the Feature Processor (from either of the combination) is fed to the Classifier which is trained off-line from the Database of similar Atherosclerotic Wall Region images. The off-line Classifier using combination one is trained from the significant features from (a) Higher Order Spectra; (b) Discrete Wavelet Transform (DWT); (c) Texture and (d) Wall Variability, selected using t-test. Using the combination two, the off-line Classifier uses grayscale features: (a) Local Binary Pattern; (b) Law's Mask Energy and (c) Wall Variability. Symptomatic ground truth information about the training, patients is drawn from cross modality imaging such as CT or MR or 3D ultrasound in the form of 0 or 1. Support Vector Machine (SVM) supervised classifier of varying kernel functions is used off-line for training. The Atheromatic™ system is also demonstrated for Radial Basis Probabilistic Neural Network (RBPNN), or Nearest Neighbor (KNN) classifier or Decision Trees (DT) Classifier for symptomatic versus asymptomatic plaque automated classification. The obtained training parameters are then used to evaluate the test set. The system also yields the cardiovascular risk score value on the basis of the four set of wall features in combination one and risk score using combination two.
  • Creation Date: 20 October 2011

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