Siemens Medical Solutions Usa, Inc.

Siemens Corporation

Acuson Corporation

Joanne Otsuki

Dorin Comaniciu

Shaohua Kevin Zhou

Constantine Simopoulos

Bogdan Georgescu

Jin-hyeong Park

Yefeng Zheng

John I Jackson

Xiaoguang Lu

Donald R Langdon

Laurence S McCabe

Feng Guo

Jin Hyeong Park

Samuel H King

David R Buscaglia

Gustavo Henrique Monteiro De Barros Carneiro

A method for view classification includes providing a frame of an object of interest, detecting a region of interest within the object of interest for each of a plurality of detectors (e.g., binary classifiers), wherein each binary classifier corresponds to a different view, performing a global view classification using a multiview classifier for each view, outputting a classification for each view, fusing outputs of the multiview classifiers, and determining and outputting a classification of the frame based on a fused output of the multiview classifiers.

Automatic cardiac view classification of echocardiography

A method and system for detection of deformable structures in medical images is disclosed. Deformable structures can represent blood flow patterns in images such as Doppler echocardiograms. A probabilistic, hierarchical, and discriminant framework is used to detect such deformable structures. This framework integrates evidence from different primitive levels via a progressive detector hierarchy, including a series of discriminant classifiers. A target deformable structure is parameterized by a multi-dimensional parameter, and primitives or partial parameterizations of the parameter are determined. An input image is received, and a series of primitives are sequentially detected using the progressive detector hierarchy, in which each detector or classifier detects a corresponding primitive. The final detector detects configuration candidates for the deformable structure.

Method and system for detection of deformable structures in medical images

A view represented by echocardiographic data is classified. A probabilistic boosting network is used to classify the view. The probabilistic boosting network may include multiple levels where each level has a multi-class local structure classifier and a plurality of local-structure detectors corresponding to the respective multiple classes. In each level, the local structure is classified as a particular view and then the local structure is detected to determine whether the currently selected local structure corresponds to the class. The view classification may be used to determine gate locations, such as a gate for spectral Doppler analysis.

Automated view classification with echocardiographic data for gate localization or other purposes

A plane position for a standard view is detected from three-dimensional echocardiographic data. The position of the plane within the volume is defined by translation, orientation (rotation), and/or scale. Possible positions are detected and other possible positions are ruled out. The classification of the possible positions occurs sequentially by translation, then orientation, and then scale. The sequential process may limit calculations required to identify the plane position for a desired view.

Automated detection of planes from three-dimensional echocardiographic data

A method for interactively detecting a maximum velocity from acquired echo signals in an ultrasound system is provided. A processor with maximum velocity code is provided. The code has a variable. A user input provides a value for the variable while the ultrasound system is in a maximum velocity mode. The ultrasound system is operated in a maximum velocity mode. The echo signals are processed to determine said maximum velocity, and the processing is adjusted while remaining in said maximum velocity mode. During CINE playback, delimeters are generated for adjusting the time period of parameter calculation.

Interactive doppler processor and method

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Oakland, CA, United States of America

Joanne Otsuki