Ultrasound carotid media wall classification and imt measurement in curved vessels using recursive refinement and validation
OwnerJasjit S. Suriinventor
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OwnerJasjit S. Suriinventor
Estimating the term…
A computer-implemented system and method for intima-media thickness (IMT) measurements using a validation embedded segmentation method. Various embodiments include receiving biomedical imaging data and patient demographic data corresponding to a current scan of a patient; checking the biomedical imaging data in real-time to determine if an artery of the patient has a calcium deposit in a proximal wall of the artery; acquiring arterial data of the patient as a combination of longitudinal B-mode and transverse B-mode data; using a data processor to automatically recognize the artery by embedding anatomic information; using the data processor to calibrate a region of interest around the automatically recognized artery; automatically computing the weak or missing edges of intima-media and media-adventitia walls using labeling and connectivity; and determining the intima-media thickness (IMT) of an arterial wall of the automatically recognized artery.
A computer-implemented method comprising:
A computer-implemented method comprising:
The method as claimed in claim 10 wherein the method where the automated recognition using higher order derivative can be with and without calcium present in the arterial proximal wall.
The method as claimed in claim 10 wherein the method where the automated recognition can be a feature based method unlike the multi-resolution higher order derivative approach. This automated recognition method guides the stage-H (calibration method) for regional classification and MA/LI reconstruction.
LI border is reconstructed from reference of MA border. The LI border is reconstructed in the same way MA border. The LI border is checked for lumen penetration and is corrected by readjusting the number of classes of MSC from three to four and re-running the MA and LI border again (recursively). This lumen is identified using a statistical classifier, just like the automated recognition (ADF profile) is validated using the statistical classifier.
Dependencies are inferred from the claim text.