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US Patent 9307926 Automatic stent detection

Patent 9307926 was granted and assigned to Volcano Corporation on April, 2016 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
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Volcano Corporation
Current Assignee
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Volcano Corporation
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
9307926
Date of Patent
April 12, 2016
Patent Application Number
14044441
Date Filed
October 2, 2013
Patent Citations Received
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US Patent 12109056 Constructing or reconstructing 3D structure(s)
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US Patent 11972568 Automated assessment of glaucoma loss from optical coherence tomography
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US Patent 12067225 Devices, systems, and methods to emphasize regions of interest across multiple imaging modalities
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US Patent 12076177 Apparatuses, systems, methods and storage mediums for performance of co-registration
0
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US Patent 11771321 System, method, and computer-accessible medium for subsurface capillary flow imaging by wavelength-division-multiplexing swept-source optical doppler tomography
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US Patent 11790523 Autonomous diagnosis of a disorder in a patient from image analysis
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US Patent 11935235 Diagnosis of a disease condition using an automated diagnostic model
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US Patent 11963740 Lumen, stent, and/or artifact detection in one or more images, such as in optical coherence tomography images
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Patent Primary Examiner
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Tom Y Lu
Patent abstract

This invention relates generally to the detection of objects, such as stents, within intraluminal images using principal component analysis and/or regional covariance descriptors. In certain aspects, a training set of pre-defined intraluminal images known to contain an object is generated. The principal components of the training set can be calculated in order to form an object space. An unknown input intraluminal image can be obtained and projected onto the object space. From the projection, the object can be detected within the input intraluminal image. In another embodiment, a covariance matrix is formed for each pre-defined intraluminal image known to contain an object. An unknown input intraluminal image is obtained and a covariance matrix is computed for the input intraluminal image. The covariances of the input image and each image of the training set are compared in order to detect the presence of the object within the input intraluminal image.

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