General Electric Company

Ge Medical Systems Information Technologies Gmbh

Robert C Gemperline

Robert D Barnes

Saad Ahmed Sirohey

Mark Niggemann

Marc B Stanis

Robert S Grant

Vinh Truong

Kumar Ramachandran

Accessing image data stored in a first storage unit () at a first location () and a second storage unit (A) at a second location (A) is provided by a common server () located at location. Available imaging data is selected by work stations (orA) based on the identification data stored in server (). Selected imaging data is then transferred to a work station from either of the storage units located at the different locations. In case a low speed network is used, transmission of the data from one location to another is facilitated by image transfer servers (andA).

Enterprise-wide data access techniques

An image data compression and decompression technique applies one or more compression code tables to optimally compress an image data stream. The compression code tables are established in accordance with anticipated image characteristics, and to accommodate different levels of variation or entropy in the image data. The image data may be divided into blocks or subregions for analysis of which of the candidate compression code tables provides the optimal compression of each subregion. The appropriate code table is selected for each subregion. The evaluation of the compression performance based upon application of each compression code table may include analysis of prediction differences or errors between predicted values for pixels of an image and the actual values for the pixels.

Image data compression employing multiple compression code tables

An image data compression technique is disclosed in which image data compression is based at least partially upon information encoded in a descriptive portion of an image data file. The descriptive portion may be a header appended to image data. In medical diagnostic applications the descriptive data may include DICOM compliant data specifying imaging modality, anatomy or other features viewable in the reconstructed images, image matrix size, and so forth. Based upon the descriptive data, the image data files are analyzed and optimal compression algorithms are identified and selected. The image data files are then compressed in accordance with the selected algorithms. The algorithms may include appropriate compression code tables, desired lengths of subregions into which the image data stream is divided for compression, predictor algorithms used to determine relative entropy levels within the image data, and so forth.

Medical image data compression employing image descriptive information for optimal compression

Image data is compressed by decomposing the image data into a plurality of resolutions or sized by wavelet decomposition, followed by compression of the resulting data sets. Adaptive Huffman code compression is optimized for compressing the data sets, predictive errors for a low frequency band and the wavelet transformed values for high frequency bands. The image data is thus compressed losslessly and in multiple resolutions such that desired resolution images can be reconstructed and transmitted in accordance with available-bandwidth and viewing capabilities.

Method and system for lossless wavelet decomposition, compression and decompression of data

An image data compression technique is provided for compressing an image data stream by selection of one or more compression algorithms from a set of candidate algorithms. Key code representative of the selected algorithm or algorithms is inserted into the compressed data stream to facilitate decompression in accordance with the selected algorithm or algorithms. The image data stream may be broken into a plurality of subregions, and compression algorithms selected for optimal compression of each subregion. Key code identifying the algorithm selected for each subregion is embedded in the compressed data stream to facilitate decompression.

Image data compression employing embedded compression and decompression codes

An image data compression technique includes division of an image data stream into a plurality of subregions and optimal compression of each subregion. The subregions may represent adjacent pixels in a reconstructed image, and may all be of the same length. The length of the subregions may be set to a default, and may be altered based upon image characteristics. The subregions are analyzed for selection of an optimal compression algorithm for each subregion from a set of candidate algorithms. Criteria for selection of the optimal algorithms may include the relative entropy levels of data within the subregions, and the length of compressed data code resulting from application of each of the candidate algorithms.

Image data compression employing optimal subregion compression

A picture archiving and communication system includes an image data compression station which received image, data files and compresses the image data. The compression is performed via one or more optimal compression algorithms for subregions of the image data. Hybrid compressed data files are created including a descriptive header and the optimally compressed image data. Code identifying the selected algorithms is inserted into the hybrid files. A server receives and stores the hybrid files, and access the files upon demand for client stations, wherein the hybrid compressed files are decompressed for viewing of the encoded images. Rapid data exchange within the system are facilitated by the use of compressed data and by the availability of descriptive data from the header portions of the files.

Growing community of inventors

Algonquin, IL, United States of America

Robert C Gemperline