Method and apparatus for generating special-purpose image analysis algorithms
Provides quantitative data about a two or more dimensional image. Classifies and counts number of entities an image contains. Each entity comprises a structure, or some other type of identifiable portion having definable characteristics. The entities located within an image may have different shape, color, texture, etc., but still belong to the same classification. Alternatively, entities comprising a similar color/texture may be classified as one type while entities comprising a different color/texture may be classified as another type. May quantify image data according to set of changing criteria and derive one or more classifications for entities in image. I.e., provides a way for a computer to determine what kind of entities (e.g., entities) are in image and counts total number of entities visually identified in image. Information utilized during a training process may be stored and applied across different images.
1. A non-transitory computer program product for automating the expert quantification of image data comprising:
a computer-readable medium encoded with computer readable instructions executable by one or more computer processors to quantify image sets comprising a locked evolving algorithm, wherein said locked evolving algorithm is generated by:
obtaining a product algorithm for analysis of a first set of image data wherein said product algorithm is configured to recognize at least one entity within said first set of image data via a training mode that utilizes iterative input to an evolving algorithm obtained from at least one first user, wherein said training mode comprises:
presenting a first set of said at least one entity to said user for feedback as to the accuracy of said first set of identified entities;
obtaining said feedback from said user;
executing said evolving algorithm using said feedback;
presenting a second set of said at least one entity to said user for feedback as to the accuracy of said second set of identified entities;
obtaining approval from said user about said second set of entities; storing said evolving algorithm as a product algorithm; and
storing said product algorithm for subsequent usage on said image sets.
2. The non-transitory computer program product of claim 1 wherein said evolving algorithm comprises a neural network.
3. The non-transitory computer program product of claim 1 wherein said evolving algorithm comprises a classification engine.
4. The non-transitory computer program product of claim 1 wherein said product algorithm comprises a pixel zoo.
5. The non-transitory computer program product of claim 1 wherein said product algorithm comprises an entity zoo.
6. The non-transitory computer program product of claim 1 , wherein said image data corresponds to histological section data.
7. The non-transitory computer program product of claim 1 , wherein said image data corresponds to material sample data.
8. The non-transitory computer program product of claim 1 , wherein said at least one entity corresponds to biological entities.
9. The non-transitory computer program product of claim 1 , wherein said at least one entity corresponds to amyloid plaques and neurofibrillary tangles.
10. A non-transitory computer program product for automating the expert quantification of image data comprising:
a computer-readable medium encoded with computer readable instructions executable by one or more computer processors to quantify image sets comprising a locked evolving algorithm, wherein said locked evolving algorithm is generated by:
obtaining mage data having a plurality of chromatic data points;
identifying which of said plurality of chromatic data points comprise an entity;
grouping said plurality of chromatic data points into a plurality of spatially connected subsets;
determining a plurality of characteristics about said spatially connected subsets;
passing said plurality of characteristics to a classification engine;
classifying said plurality of spatially connected subsets into at least one classification;
obtaining affirmation of the veracity of said at least one classification from a user;
evaluating said spatially connected subset to derive a set of relative harmonic amplitudes;
passing said relative harmonics into a neural network, wherein said neural network is trained to classify said spatially connected subsets using shape information provided by said set of relative harmonic amplitudes;
presenting a result of said classification to said user;
obtaining verification of said classification from said user;
using said verification to adjust said neural network; and
storing said neural network for subsequent usage on said image sets.
11. The non-transitory computer program product of claim 10 , wherein said plurality of characteristics comprises color and wherein at least one classification differ by color.
12. The non-transitory computer program product of claim 10 , wherein said plurality of characteristics comprises shape and wherein said at least one classification differ by shape.
13. The non-transitory computer program product of claim 10 , wherein said plurality of characteristics comprises texture and wherein said at least one classification differ by texture.
14. The non-transitory computer program product of claim 10 , wherein said plurality of characteristics comprises two or more sets of characteristics, wherein a first stage of processing classifies image data based on a first set of characteristics and a second stage of processing classifies image data based on a second set of characteristics.