Automatic graph scoring for neuropsychological assessments

Patent metadata

Field	Value
Publication number	US-2021073981-A1
Application number	US-202016953150-A
Country	US
Kind code	A1
Filing date	Nov 19, 2020
Priority date	Apr 2, 2018
Publication date	Mar 11, 2021
Grant date	—

Abstract

Systems and methods of the present invention provide for: receiving a digital image data; modifying the digital image data to reduce a width of a feature within the digital image data; executing a dimension reduction process on the feature; storing a feature vector comprising: at least one feature for each of the received digital image data, and a correct or incorrect label associated with each feature vector; selecting the feature vector from a data store; training a classification software engine to classify each feature vector according to the label; classifying the image data as correct or incorrect according to a classification software engine; and generating an output labeling a second digital image data as correct or incorrect.

First claim

The invention claimed is:

1 . A system comprising a server, comprising a hardware computing device coupled to a network and including at least one processor executing instructions within a memory which, when executed, cause the system to:
receive a digital image data; for each of a first plurality of pixels within the digital image data:
identify a pixel within the first plurality of pixels;
identify a total number of black pixels within:
a second plurality of pixels immediately adjacent to the pixel; and
a third plurality of pixels immediately adjacent to the second plurality of pixels;

determine whether the total number of black pixels is greater than a threshold number;
responsive to a determination that the total number of black pixels is greater than the threshold number, characterize the pixel as a black pixel; and
responsive to a determination that the total number of black pixels is not greater than the threshold number, characterize the pixel as a white or an empty pixel.

2 . The system of claim 1 , wherein the instructions further cause the system to:
receive a plurality of image inputs, wherein the digital image data is one of a plurality of image data, each digital image data being associated with one of the plurality of image inputs; execute a dimension reduction process on at least one feature within the digital image data; store, within a data store:
a feature vector for each digital image data associated with each of the plurality of image inputs, the feature vector comprising the at least one feature of the digital image data; and
a correct or incorrect label associated with each feature vector;
select, from the data store, the feature vector for each digital image data; and train a classification software engine to classify each feature vector according to the label associated with the feature vector.

3 . The system of claim 2 , wherein the instructions further cause the system to:
receive a second image input comprising a second digital image data; classify the second digital image data as correct or incorrect according to the classification software engine; and generate an output labeling the second digital image data as correct or incorrect.

4 . The system of claim 2 , wherein the instructions further cause the system to, for each of a first plurality of pixels within the digital image data:
identify a pixel within the first plurality of pixels; identify a total number of black pixels within a second plurality of pixels immediately adjacent to the pixel and a third plurality of pixels immediately adjacent to the second plurality of pixels determine whether the total number of black pixels is greater than a threshold number; responsive to a determination that the total number of black pixels is greater than the threshold number, characterize the pixel as a black pixel; responsive to a determination that the total number of black pixels is not greater than the threshold number, characterize the pixel as a white or an empty pixel.

5 . The system of claim 2 , wherein the dimension reduction process includes execution of an orthogonal transformation to reduce a number of the at least one feature within the feature vector data for each digital image data.

6 . The system of claim 2 , wherein:
each at least one feature is a pixel in the digital image data; and the dimension reduction algorithm reduces the number of pixels and features in the feature vector.

7 . The system of claim 2 , wherein the classification software engine uses the feature vector to train the model with a non-linear Radial Basis Function (RBF) kernel method.

8 . The system of claim 2 , wherein the classification software engine is trained by:
partitioning the plurality of feature vectors into a plurality of folds; designating a fold within the plurality of folds as a validation dataset; designating each remaining fold in the plurality of folds as a training dataset; running a performance analysis for each of a plurality of subsets in the training dataset, the performance analysis comprising:
performing an analysis on each of the plurality of training subsets in the training dataset; and
validating the analysis against the validation dataset;
designating each remaining fold in the plurality of folds as the validation dataset, and the validation dataset as being within the training dataset; and repeating the performance analysis for each remaining fold in the plurality of folds.

9 . A method comprising:
receiving, by a server comprising a hardware computing device coupled to a network and including at least one processor executing instructions within a memory, a digital image data; for each of a first plurality of pixels within the digital image data:
identifying, by the server, a pixel within the first plurality of pixels;
identifying, by the server, a total number of black pixels within:
a second plurality of pixels immediately adjacent to the pixel; and
a third plurality of pixels immediately adjacent to the second plurality of pixels;

determining, by the server, whether the total number of black pixels is greater than a threshold number;
responsive to a determination that the total number of black pixels is greater than the threshold number, characterizing, by the server, the pixel as a black pixel; and
responsive to a determination that the total number of black pixels is not greater than the threshold number, characterizing, by the server, the pixel as a white or an empty pixel;

10 . The method of claim 9 , further comprising the steps of:
receiving, by the server, a plurality of image inputs, wherein the digital image data is one of a plurality of image data, each digital image data being associated with one of the plurality of image inputs; executing, by the server, a dimension reduction process on at least one feature within the digital image data; storing, by the server, within a data store:
a feature vector for each digital image data associated with each of the plurality of image inputs, the feature vector comprising the at least one feature of the digital image data; and
a correct or incorrect label associated with each feature vector;
selecting, by the server, from the data store, the feature vector for each digital image data; and training, by the server, a classification software engine to classify each feature vector according to the label associated with the feature vector.

11 . The method of claim 10 , further comprising the steps of:
receiving, by the server, a second image input comprising a second digital image data; classifying, by the server, the second digital image data as correct or incorrect according to the classification software engine; and generating, by the server, an output labeling the second digital image data as correct or incorrect.

12 . The method of claim 10 , further comprising the steps of, for each of a first plurality of pixels within the digital image data:
identifying, by the server, a pixel within the first plurality of pixels; identifying, by the server, a total number of black pixels within a second plurality of pixels immediately adjacent to the pixel and a third plurality of pixels immediately adjacent to the second plurality of pixels determining, by the server, whether the total number of black pixels is greater than a threshold number; responsive to a determination that the total number of black pixels is greater than the threshold number, characterizing by the server, the pixel as a black pixel; responsive to a determination that the total number of black pixels is not greater than the threshold number, characterizing, by

Assignees

• Pearson Education Inc

Inventors

• Wang Zhaodong

Classifications

• G06V10/764 Primary

  using classification, e.g. of video objects · CPC title

• G06V10/7715

  Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods · CPC title

• G06T7/0012 Primary

  Biomedical image inspection · CPC title

• G06F18/2431

  Multiple classes · CPC title

• G06F18/217

  Validation; Performance evaluation; Active pattern learning techniques · CPC title