Abstract: Improved localization of the capsule in acoustic capsule endoscopy is provided by using analysis of the frames of the acoustic images to deduce the relative motion of the capsule from frame to frame. This idea can be supplemented with any combination of: further localization methods; propulsion of the capsule via acoustic radiation reaction; bidirectional communication and system level feedback control; energy harvesting; photoacoustic (or x-ray acoustic) imaging; and adding therapy and/or sensor capabilities to the capsule.

Abstract: Improved localization of the capsule in acoustic capsule endoscopy is provided by using analysis of the frames of the acoustic images to deduce the relative motion of the capsule from frame to frame. This idea can be supplemented with any combination of: further localization methods; propulsion of the capsule via acoustic radiation reaction; bidirectional communication and system level feedback control; energy harvesting; photoacoustic (or x-ray acoustic) imaging; and adding therapy and/or sensor capabilities to the capsule.

Abstract: Peak blood velocity measurement for automated stenosis detection is provided. Ultrasound measurements of the peak blood velocity are corrected by a calculation of the Doppler angle, which exists from misalignment of the ultrasound transducer axis and the true blood velocity. The direction of the blood velocity and the Doppler angle are found by imaging a set of planar cross-sections of a blood vessel, such as the carotid artery, to obtain velocity maps of the blood flowing in the blood vessel. Peak blood velocity can be correlated with an amount of stenosis therefore accurate peak blood velocity measurements are necessary for medical diagnosis. Automated stenosis detection allows for implementation in many medical settings. A capacitive micromachined ultrasound transducer array is also provided to measure the planar cross-sectional images.

Abstract: A method to detect and classify a structure of interest in a medical image is provided to enable high specificity without sacrificing the sensitivity of detection. The method is based on representing changes in three-dimensional image data with a vector field, characterizing the topology of this vector field and using the characterized topology of the vector field for classification of a structure of interest. The method could be used as a stand-alone method or as a post-processing method to enhance and classify outputs of a high-sensitivity low-specificity method to eliminate false positives.

Abstract: An automatic method for the registration of prone and supine computed tomographic colonography data is provided. The method improves the radiologist's overall interpretation efficiency as well as provides a basis for combining supine/prone computer-aided detection results automatically. The method includes determining (centralized) paths or axes of the colon from which relatively stationary points of the colon are matched for both supine and prone positions. Stretching and/or shrinking of either the supine or prone path perform registration of these points. The matching and registration occurs in an iterative and recursive manner and is considered finished based on one or more decision criteria.

Abstract: A method to detect and classify a structure of interest in a medical image is provided to enable high specificity without sacrificing the sensitivity of detection. The method is based on representing changes in three-dimensional image data with a vector field, characterizing the topology of this vector field and using the characterized topology of the vector field for classification of a structure of interest. The method could be used as a stand-alone method or as a post-processing method to enhance and classify outputs of a high-sensitivity low-specificity method to eliminate false positives.

Abstract: An automatic method for the registration of prone and supine computed tomographic colonography data is provided. The method improves the radiologist's overall interpretation efficiency as well as provides a basis for combining supine/prone computer-aided detection results automatically. The method includes determining (centralized) paths or axes of the colon from which relatively stationary points of the colon are matched for both supine and prone positions. Stretching and/or shrinking of either the supine or prone path perform registration of these points. The matching and registration occurs in an iterative and recursive manner and is considered finished based on one or more decision criteria.

Abstract: A computer-implemented method for automatically detecting shapes in a medical image is provided. The method is based on the concept that normals to a surface intersect or nearly intersect with neighboring normals depending on the curvature features of the surface. The method first locates a surface in a medical image after which normal vectors are generated to the located surface. Then the method identifies at least one intersection and/or near intersection of the normal vectors. The key idea is that the number of intersections identifies shapes such as potential malignant candidates. The method also includes the step of scaling normal vectors to provide additional robustness to the shape detection. The method eliminates viewing of large segments of images, thereby markedly shortening interpretation time and improving accuracy of detection. It also provides for an early detection of precancerous growths so that they can be removed before evolving into a frank malignancy.