Siemens Medical Solutions Usa, Inc.

Siemens Corporate Research, Inc.

Siemens Aktiengesellschaft

Siemens Corporation

Thomas Moeller

Gianluca Paladini

Cheng-Chung Liang

Mathia Scucchiari

In response to a request from a client, a server for volume rendering loads a volume dataset from a storage archive, creates a low resolution sub-sampled copy of such volume dataset and transmits it to a client. In response to subsequent requests from the client, the server for volume rendering renders a high quality image of the full resolution volume dataset and renders a low quality image of the sub-sampled copy of the volume dataset, then generates a pixel mask or a hybrid pixel mask indicative of a difference between the high quality image and the low quality image and transmits such pixel mask to the client. The client receives from the server the pixel mask and computes a high quality image based at least in part on the pixel mask and on a selective rendering of its local low resolution sub-sampled copy of the volume dataset.

Method and apparatus for efficient client-server visualization of multi-dimensional data

A method for managing a memory system for large data volumes includes providing a central memory management system comprising a memory management interface between applications and a memory of a programmed computer, maintaining a global priority list of data buffers allocated by the applications, storing decompressed data of the data buffers into a cache which is managed by the central memory management system using a separate priority list, and accessing the decompressed data of the data buffers in the cache.

Advanced memory management architecture for large data volumes

An efficient memory management method for handling large data volumes, comprising a memory management interface between a plurality of applications and a physical memory, determining a priority list of buffers accessed by the plurality of applications, providing efficient disk paging based on the priority list, ensuring sufficient physical memory is available, sharing managed data buffers among a plurality of applications, mapping and unmapping data buffers in virtual memory efficiently to overcome the limits of virtual address space.

A system and method for automatic bone extraction from a medical image is provided. A method for automatically extracting a bone from a medical image, comprises: performing a thresholding on the image in an intensity range of the bone to generate a first bit mask; eroding the first bit mask to remove connections between blood vessels and the bone to generate a second bit mask; performing a region growing on the second bit mask starting from a seed point within the intensity range to separate the bone from unconnected blood vessels and to generate a third bit mask; dilating the third bit mask to generate a fourth bit mask; and performing a region growing on the fourth bit mask starting from the seed point within the intensity range to generate a fifth bit mask representing the bone.

System and method for automatic bone extraction from a medical image

The present invention relates to a method and a system for extracting a 3D region of interest from a stack of medical scan slices which is still a challenging task even in this new millennium. One difficulty is that there is no robust automatic algorithm that can handle all the different situations and applications. Human intervention is usually unavoidable for extracting desired area or organs. However, without convenient tools, it is a tedious job for human being to go through hundred or thousand of slices just to extract region of interest. A system combines and modifies several advanced techniques to help a user extract volume of interest very easily and quickly. The technique of livewire for initial-delineate and modify regions of interest from 2D slices is utilized. Then variational interpolation technique is employed to derive a 3D shape from these delineated 2D contours. The 3D shape can be visualized as is or converted to a 3D solid for volume visualization or masking out of the undesirable parts of a data set.

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Princeton, NJ, United States of America

Thomas Moeller