MEDICAL IMAGING DATA COMPRESSION UTILIZING CODEBOOKS

    公开(公告)号:US20240211133A1

    公开(公告)日:2024-06-27

    申请号:US18434756

    申请日:2024-02-06

    Abstract: Compression of medical imaging data using codebooks and entropy encoding. Medical imaging data such as tomosynthesis imagery data may be compressed using codewords based on frequency analysis. In an implementation sequential registration technique may be applied to the medical imaging data to create a plurality transformation matrices. The plurality of transformation matrices may be compressed using a matrix codebook. The compressed medical imaging data may be represented as an image codebook and the matrix codebook, providing secure storage and lossless compression of sensitive medical information.

    System and method for random-access manipulation of compacted data files

    公开(公告)号:US11899624B2

    公开(公告)日:2024-02-13

    申请号:US18078909

    申请日:2022-12-09

    CPC classification number: G06F16/1752 G06F3/067 G06F3/0608 G06F3/0641

    Abstract: A system and method for random-access manipulation of compacted data files, utilizing a reference codebook, a random-access engine, a data deconstruction engine, and a data deconstruction engine. The system may receive a data query pertaining to a data read or data write request, wherein the data file to be read from or written to is a compacted data file. A random-access engine may facilitate data manipulation processes by accessing a reference codebook associated with the compacted data file, a frequency table used to construct the reference codebook, and data query details. A data read request is supported by random-access search capabilities that may enable the locating and decoding of the bits corresponding to data query details. A random-access engine facilitates data write processes. The random-access engine may encode the data to be written, insert the encoded data into a compacted data file, and update the codebook as needed.

    System and method for data compaction utilizing mismatch probability estimation

    公开(公告)号:US11687241B2

    公开(公告)日:2023-06-27

    申请号:US17974230

    申请日:2022-10-26

    Abstract: A system and method for compacting data that uses mismatch probability estimation to improve entropy encoding methods to account for, and efficiently handle, previously-unseen data in data to be compacted. Training data sets are analyzed to determine the frequency of occurrence of each sourceblock in the training data sets. A mismatch probability estimate is calculated comprising an estimated frequency at which any given data sourceblock received during encoding will not have a codeword in the codebook. Entropy encoding is used to generate codebooks comprising codewords for data sourceblocks based on the frequency of occurrence of each sourceblock. A “mismatch codeword” is inserted into the codebook based on the mismatch probability estimate to represent those cases when a block of data to be encoded does not have a codeword in the codebook. During encoding, if a mismatch occurs, a secondary encoding process is used to encode the mismatched sourceblock.

    System and method for data compaction and security using multiple encoding algorithms

    公开(公告)号:US11620051B2

    公开(公告)日:2023-04-04

    申请号:US17727913

    申请日:2022-04-25

    Abstract: A system and method for encoding data using a plurality of encoding libraries. Portions of the data are encoded by different encoding libraries, depending on which library provides the greatest compaction for a given portion of the data. This methodology not only provides substantial improvements in data compaction over use of a single data compaction algorithm with the highest average compaction, but provides substantial additional security in that multiple decoding libraries must be used to decode the data. In some embodiments, each portion of data may further be encoded using different sourceblock sizes, providing further security enhancements as decoding requires multiple decoding libraries and knowledge of the sourceblock size used for each portion of the data. In some embodiments, encoding libraries may be randomly or pseudo-randomly rotated to provide additional security.

    System and method for random-access manipulation of compacted data files

    公开(公告)号:US11609882B2

    公开(公告)日:2023-03-21

    申请号:US17734052

    申请日:2022-04-30

    Abstract: A system and method for random-access manipulation of compacted data files, utilizing a reference codebook, a random-access engine, a data deconstruction engine, and a data deconstruction engine. The system may receive a data query pertaining to a data read or data write request, wherein the data file to be read from or written to is a compacted data file. A random-access engine may facilitate data manipulation processes by accessing a reference codebook associated with the compacted data file, a frequency table used to construct the reference codebook, and data query details. A data read request is supported by random-access search capabilities that may enable the locating and decoding of the bits corresponding to data query details. A random-access engine facilitates data write processes. The random-access engine may encode the data to be written, insert the encoded data into a compacted data file, and update the codebook as needed.

    System and method for computer data type identification

    公开(公告)号:US11609881B2

    公开(公告)日:2023-03-21

    申请号:US17727919

    申请日:2022-04-25

    Abstract: A system and method for file type identification involving extraction of a file-print of a file, the file-print being a unique or practically-unique representation of statistical characteristics associated with the distribution of bits in the binary contents of the file, similar to a fingerprint. The file-print is then passed to a machine learning algorithm that has been trained to recognize file types from their file-prints. The machine learning algorithm returns a predicted file type and, in some cases, a probability of correctness of the prediction. The file may then be encoded using an encoding algorithm chosen based on the predicted file type.

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