Abstract: The present technique selectively handles image data, which is decomposed into a plurality of resolution levels. In one embodiment, an image resolution suitable for display in a desired viewport is selected via an iterative process including determining whether scaling of one of the different image resolutions of the plurality of different image resolutions by a predetermined scaling threshold would result in a scaled image resolution that is less than or equal to the resolution of the desired viewport. A portion of the plurality of data sets may be selectively requested for recomposition of the image at the image resolution. Additional systems, methods, and devices for handling image data are also disclosed.

Abstract: The present technique addressably handles image data, which is decomposed and tessellated into a plurality of tessellated sub-band blocks. The tessellated sub-band blocks may be addressed by an array of indices, which identify specific data blocks of the image data by decomposition level and spatial coordinates of the tessellated sub-band blocks. Accordingly, a desired region of the image data at multiple resolutions can be identified and individually handled for storage, transmission, retrieval, and display. The desired region also can be reference marked based on the array of indices.

Abstract: The present technique selectively handles image data, which is decomposed into a plurality of resolution levels. The image data may be retrieved in order of progressively higher resolution levels for display in a viewport. Initially, the image data may be displayed in the viewport at a relatively lower one of the plurality of resolution levels. As a user desires higher resolution displays of all or part of the image, one or more of the progressively higher resolution levels may be retrieved and scaled to fit the viewport. Accordingly, the present technique handles the data more efficiently by accessing the image data in blocks corresponding to the plurality of resolution levels, rather than accessing the entirety of the image data. A scaling threshold also can be used to signal the need for a next higher level of the plurality of resolution levels.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method and apparatus are disclosed for providing fully integrated information processing, management and communication functions in a fully integrated RIS-PACS system for a radiology department/healthcare environment by employing brokerless interface methods to synchronize patient and exam data entities in a radiology information system (RIS) database and a picture archive and communication system (PACS) database within the RIS-PACS system and to generate direct database calls to the RIS and PACS databases. The running of RIS and PACS applications within the RIS-PACS system is initiated from client workstations over a web interface. Information from the databases is accessed by the client workstations. Also, the system may communicate between internal PACS applications and an external RIS system over a HL7-based interface or between internal RIS applications and an external PACS system over a standard medical communications interface such as HL7 and DICOM.

Abstract: The present technique addressably handles image data, which is decomposed and tessellated into a plurality of tessellated sub-band blocks. The tessellated sub-band blocks may be addressed by an array of indices, which identify specific data blocks of the image data by decomposition level and spatial coordinates of the tessellated sub-band blocks. Accordingly, a desired region of the image data at multiple resolutions can be identified and individually handled for storage, transmission, retrieval, and display. The desired region also can be reference marked based on the array of indices.

Abstract: The present technique selectively handles image data, which is decomposed into a plurality of resolution levels. The image data may be retrieved in order of progressively higher resolution levels for display in a viewport, which may have a viewport resolution that is higher or lower than a desired one of the resolution levels. Initially, the image data may be displayed in the viewport at a relatively lower one of the plurality of resolution levels. As a user desires higher resolution displays of all or part of the image, one or more of the progressively higher resolution levels may be retrieved and scaled to fit the viewport. Accordingly, the present technique handles the data more efficiently by accessing the image data in blocks corresponding to the plurality of resolution levels, rather than accessing the entirety of the image data. A scaling threshold also can be used to signal the need for a next higher level of the plurality of resolution levels.

Abstract: The present technique addressably handles image data, which is decomposed and tessellated into a plurality of tessellated sub-band blocks. The tessellated sub-band blocks may be addressed by an array of indices, which identify specific data blocks of the image data by decomposition level and spatial coordinates of the tessellated sub-band blocks. Accordingly, a desired region of the image data at multiple resolutions can be identified and individually handled for storage, transmission, retrieval, and display. The desired region also can be reference marked based on the array of indices.