Abstract: A computer-based system for creating and using intelligent databases for assisting in intima-media thickness (IMT) measurements. Various embodiments utilize clinical data (images) to design intelligent clinical data sets, including geometric markers like blood vessel centerline, which can then be used with conventional image processing to compute the intima-media thickness of the carotid walls. The intelligent clinical image data utilizes the demographics (ethnity/race, gender and age) to classify the data. Finally, the intelligent database is used to collect intelligent parameters which are then applied to the incoming patients to estimate their intima-media thickness.

Abstract: 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 edge flow, labeling and connectivity; and determining the intima-media thickness (IMT) of an arterial wall of the automatically recognized artery.

Abstract: Characterization of carotid atherosclerosis and classification of plaque into symptomatic or asymptomatic and risk score estimation are of clinical value. A statistical system is described for symptomatic versus asymptomatic plaque automated classification of carotid ultrasound images and cardiovascular risk score computation. The technique is applicable for the following types of modalities for carotids: 2D Ultrasound, 3D Ultrasound, CT, MR. Wall region is segmented and features are extracted consisting of type 1 combination consisting of: (a) Higher Order Spectra; (b) Discrete Wavelet Transform (DWT); (c) Texture and (d) Wall Variability; type 2 combination consisting of: (a) Local Binary Pattern; (b) Law's Mask Energy and (c) Wall Variability and type 3 combination: (a) Trace Transform; (b) Fuzzy Grayscale Level Co-occurrence Matrix and (c) Wall Variability. These features are trained using a training classifier on training images and the coefficients are applied to on-line test patient images.

Abstract: A computer-implemented system and method for fast, reliable, and automated embodiments for using a multi-resolution edge flow approach to vascular ultrasound for intima-media thickness (IMT) measurement. 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; 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 edge flow, labeling and connectivity; and determining the intima-media thickness (IMT) of an arterial wall of the automatically recognized artery.

Abstract: Automated IMT system hypothesize that far wall of the common carotid artery has the highest intensity. In this current application, we verify that this hypothesis holds true for B-mode or RF-mode longitudinal ultrasound images of the carotid wall. The methodology consists of generating the composite image (arithmetic sum of images) from the database by first registering the carotid image frames with respect to a nearly straight carotid artery frame from the same database using (a) B-spline based non-rigid registration and (b) affine registration. Prior to registration, we segment the carotid artery lumen using a level set based algorithm followed by morphological image processing. The binary lumen images are registered and the transformations are applied to the original grayscale CCA images. These B-mode or RF-mode ultrasound images are then used for IMT computation using automated methods which hypothesize that far wall has the brightest intensity distribution.