Computing technologies for image operations
A method comprises: receiving, via a processor, an image depicting a tissue; quantifying, via the processor, the image based on: segmenting, via the processor, the image into a plurality of segments; identifying, via the processor, a plurality of histological elements in the segments; forming, via the processor, a network graph comprising a plurality of nodes, wherein the histological elements correspond to the nodes; measuring, via the processor, a feature of the network graph; performing, via the processor, a transformation on the image based on the feature; determining, via the processor, a non-parametric feature of the image based on the transformation; saving, via the processor, the non-parametric feature onto a database.
1. A method comprising:
receiving, via a processor, an image depicting a tissue;
quantifying, via the processor, the image based on:
segmenting, via the processor, the image into a plurality of segments;
identifying, via the processor, a plurality of histological elements in the segments;
forming, via the processor, a network graph comprising a plurality of nodes, wherein the histological elements correspond to the nodes;
measuring, via the processor, a feature of the network graph;
performing, via the processor, a transformation on the image based on the feature;
determining, via the processor, a non-parametric feature of the image based on the transformation;
saving, via the processor, the non-parametric feature onto a database.
2. The method of claim 1 , wherein the image is a whole slide image.
3. The method of claim 1 , wherein the segment depicts at least one of a nucleus, a cell, or a gland.
4. The method of claim 3 , wherein the quantifying comprises performing, via the processor, a morphometric process on the at least one of the nucleus, the cell, or the gland, wherein the identifying is based on the morphometric process.
5. The method of claim 1 , wherein the transformation is at least one of a Wavelet transformation, a Fourier transformation, or a Chebyshev transformation, wherein the transformation is on the image as a whole.
6. The method of claim 1 , wherein the non-parametric feature comprises a signal processing metric.
7. The method of claim 1 , further comprising:
training, via the processor, a machine learning technique based on the non-parametric feature.
8. The method of claim 1 , wherein the saving comprises storing, via the processor, the non-parametric feature in a record of the database, wherein the record is associated with the image and with a plurality of images depicting a plurality of tissues, wherein the tissue and the tissues are obtained from a biological entity, wherein the record comprises information about the image and the images, information about a case, and information about an interaction of a user with a server comprising the processor, wherein the interaction is with respect to at least one of the case, the image, wherein the record corresponds to the case.
9. The method of claim 8 , further comprising:
facilitating, via the processor, a maintenance of a block chain for the record.
10. The method of claim 9 , wherein the record comprises a category, wherein the maintenance comprises maintaining the block chain for the category, wherein the category is image-based.
11. The method of claim 1 , wherein the database is configured to directly interface with a client such that the client is able to request a number of bytes from a location in the database and thereby receive a data of a tile associated with the image.
12. The method of claim 1 , wherein the database is an in-memory database.
13. The method of claim 1 , further comprising:
facilitating, via the processor, a maintenance of a block chain for the image.