International Business Machines Corporation

Siemens Product Lifecycle Management Software Inc.

Quan Wang

Tanveer Fathima Syeda-Mahmood

David James Beymer

Robert Kimberlin Foster

Patrick McNeillie

Van Hoa Lee

Ping Chen

Priya Paul

Dijia Wu

Le Lu

Christine Iju Wang

Meizhu Liu

Casey Lee McCreary

Joy Mei-Jen Underhill

Kevin Shaohua Zhou

Xiaodong Xia

Natalie Marie Post

Embodiments of the present invention relate to discriminating between normal and abnormal left ventricles in echocardiography. In some embodiments, a first region of a first image of a heart is located by clustering. The first region depicts a chamber of the heart. The chamber has a chamber shape. A boundary of the first region is determined by contour tracing a retaining the traced contour overlapping the first region. A predetermined shape is compared to the boundary. The predetermined shape had been determined without reference to the first image. A first plurality of parameters is determined that, when applied to the predetermined shape, conforms the predetermined shape to the boundary and minimizes at least one error between the predetermined shape and the boundary. An indication of normality or abnormality is determine from the first plurality of parameters.

Discriminating between normal and abnormal left ventricles in echocardiography

Embodiments of the present invention relate to discriminating between normal and abnormal left ventricles in echocardiography. In one embodiment, a method of and computer program product for discriminating between normal and abnormal left ventricles in echocardiography are provided. A first region of a first image of a heart is located. The first region depicts a chamber of the heart. A boundary of the first region is determined. A predetermined shape is compared to the boundary. A first plurality of parameters is determined that, when applied to the predetermined shape, conforms the predetermined shape to the boundary. The first plurality of parameters is provided to a classifier. An indication of normality or abnormality is received from the classifier.

Methods and systems for automatic classification of images of internal structures of human and animal bodies. A method includes receiving a magnetic resonance (MR) image testing model and determining a testing volume of the testing model that includes areas of the testing model to be classified as bone or cartilage. The method includes modifying the testing model so that the testing volume corresponds to a mean shape and a shape variation space of an active shape model and producing an initial classification of the testing volume by fitting the testing volume to the mean shape and the shape variation space. The method includes producing a refined classification of the testing volume into bone areas and cartilage areas by refining the boundaries of the testing volume with respect to the active shape model and segmenting the MR image testing model into different areas corresponding to bone areas and cartilage areas.

Automatic spatial context based multi-object segmentation in 3D images

A method, apparatus, and computer instructions for improving performance and scalability of common information model object (CIMOM) by using a hardware management console (HMC) repository. The HMC repository includes storage of HMC objects, an HMC object model and a common interface (CIM Wrapper) for accessing to HMC objects. The HMC object model allows the user to store and retrieve the CIM objects and their associations efficiently. A CIM provider may access HMC objects through a common interface (CIM Wrapper), so that different repositories are allowed to be used without significant changes to the underlying server code (CIM provider). A CIM provider may also call the HMC repository directly for more efficient operations because the number of times conversions made between the CIM object and the repository object are greatly decreased.

Method and apparatus for improving performance and scalability of an object manager

A method, system, and product are described for creating and managing affinity between memory and processors in logical partitions in a data processing system. The data processing system includes multiple processors. A memory affinity data structure is established. The memory affinity data structure identifies ones of the processors that have a close affinity with each one of multiple regions of the system memory. A memory affinity parameter is established and is utilized to determine whether memory affinity is required for each one of the logical partitions. In response to a determination that memory affinity is required for one of the logical partitions, the memory affinity data structure is utilized by a partition manager for the logical partition to allocate an optimal amount of memory that has a close affinity to ones of the processors that are assigned to the logical partition.

Method, system, and computer program product for creating and managing memory affinity in logically partitioned data processing systems

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Troy, NY, United States of America

Quan Wang