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1. WO2017129573 - IMAGE DATA PRE-PROCESSING

Note: Text based on automatic Optical Character Recognition processes. Please use the PDF version for legal matters

CLAIMS:

1. A computer-implemented method (40) of reducing processing time of an application for visualizing image data, wherein the application is one of a plurality of applications selectable by a user, wherein each of the plurality of applications comprises a pre-processing algorithm for pre-processing the image data, wherein the method comprises predicting which one of the pre-processing algorithms is to be performed in response to a selection of an application by the user by:

extracting (421) a feature vector from the image data, metadata, and/or additional data associated with the image data,

supplying (422) the feature vector as input to a machine learned model, and - receiving (423) an algorithm identifier as output from the machine learned model, the algorithm identifier identifying the pre-processing algorithm,

the method further comprising:

using (424) the algorithm identifier to select (42) the pre-processing algorithm, thereby obtaining a selected pre-processing algorithm, and

- pre-processing (43) the image data using the selected pre-processing algorithm.

2. The method according to claim 1, wherein the image data is DICOM image data, wherein the metadata is a DICOM header of the DICOM image data, and wherein the extracting the feature vector comprises extracting DICOM text tags from the DICOM header.

3. The method according to claim 2, wherein the DICOM text tags are free-text DICOM tags, and wherein the extracting the feature vector comprises processing the free-text DICOM tags to obtain a set of processed DICOM text tags.

4. The method according to claim 3, wherein the extracting the feature vector comprises using a random forest machine learning technique to generate the feature vector from the set of processed DICOM text tags.

5. The method according to any of the preceding claims, wherein the machine learned model is produced by a machine learning system (23).

6. The method according to claim 5, wherein the machine learning system comprises a machine learning engine configured for building a machine learned model by using a feature vector as input vector and receiving at least one algorithm identifier as output vector, wherein the feature vector is extracted from the image data, metadata, and/or additional data associated with the image data.

7. The method according to claim 6, wherein the feature vector is extracted from the image data, metadata and/or additional data associated with the image data from a single picture archiving and communication system.

8. The method according to any of the preceding claims, wherein a processing algorithm is configured for leaving the image data unaltered.

9. A computer readable medium comprising transitory or non-transitory data representing instructions arranged to cause a processor to carry out the method according to any of the preceding claims.

10. A device configured for reducing processing time of an application for visualizing image data, wherein the application is one of a plurality of applications selectable by a user, wherein each of the plurality of applications comprises a pre-processing algorithm for pre-processing the image data, the device comprising:

- an input interface configured to access the image data, metadata, and/or additional data associated with the image data;

a memory comprising application data representing the plurality of applications and instruction data representing a set of instructions; and

a processor configured to communicate with the input interface and the memory and to execute the set of instructions, wherein the set of instructions, when executed by the processor, cause the processor to predict which one of the pre-processing algorithms is to be performed in response to a selection of an application by the user by:

extracting a feature vector from the image data, metadata, and/or additional data associated with the image data,

supplying the feature vector as input to a machine learned model, wherein the machine learned model is stored as model data in the memory, and

receiving an algorithm identifier as output from the machine learned model, the algorithm identifier identifying the pre-processing algorithm;

the set of instructions, when executed by the processor, further causing the processor to:

use the algorithm identifier to select the pre-processing algorithm, thereby obtaining a selected pre-processing algorithm, and

pre-process the image data using the selected pre-processing algorithm.

11. The device according to claim 10 being a workstation or imaging apparatus.