Diagnosis Support System Providing Guidance to a User by Automated Retrieval of Similar Cancer Images with User Feedback

• US 20120283574A1
• Filed: 05/04/2012
• Published: 11/08/2012
• Est. Priority Date: 05/06/2011
• Status: Abandoned Application

• Current Assignee: STI Medical Systems LLC
• Original Assignee: STI Medical Systems LLC
• Inventors: Wolters, Rolf Holger, Park, Sun Young, Sargent, Dustin Michael, Gustafsson, Ulf Peter
• Application Number:
  US13/464,914
• Publication Number:
  US 20120283574A1
• Time in Patent Office: Days
• US Class Current: 600/476
• CPC Class Codes: G06F 16/5838 using colour
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06F 18/253 of extracted features
• CPC Class Codes: G06T 7/0014 using an image reference approach
• CPC Class Codes: G06V 10/40 Extraction of image or video features
• CPC Class Codes: G06V 10/761 Proximity, similarity or dissimilarity measures
• CPC Class Codes: G06V 10/762 using clustering, e.g. of similar faces in social networks
• CPC Class Codes: G06V 10/806 of extracted features
• CPC Class Codes: G16H 30/40 for processing medical images, e.g. editing

Abstract

The present invention is a diagnosis support system providing automated guidance to a user by automated retrieval of similar disease images and user feedback. High resolution standardized labeled and unlabeled, annotated and non-annotated images of diseased tissue in a database are clustered, preferably with expert feedback. An image retrieval application automatically computes image signatures for a query image and a representative image from each cluster, by segmenting the images into regions and extracting image features in the regions to produce feature vectors, and then comparing the feature vectors using a similarity measure. Preferably the features of the image signatures are extended beyond shape, color and texture of regions, by features specific to the disease. Optionally, the most discriminative features are used in creating the image signatures. A list of the most similar images is returned in response to a query. Keyword query is also supported.

Claims

1. A diagnosis support system for a user, comprising:

a database containing high-resolution standardized database images that have been clustered into clusters, each cluster having a cluster feature vector computed by image features in regions in said database images in said cluster, using an overlapping clustering algorithm that allows database images to be assigned to more than one cluster; and

a query-by-example image retrieval application that applies a similarity measure between a query feature vector computed by image features in regions in a query image, and said cluster feature vectors;

wherein said feature vectors are automatically computed by quantitatively describing image signatures for said images by;

image segmentation into regions; and

feature extraction of image features in said regions to compute said feature vectors;

wherein said query-by-example image retrieval application returns a list of database images similar to said query image, ranked by similarity.

2. A diagnosis support system according to claim 1, wherein said database images have been clustered by cluster feature vectors that have been computed using features that are most discriminative between cluster feature vectors.

3. A diagnosis support system according to claim 1, wherein said similarity measure comprises a combination of similarity measures selected from the group consisting of linear combination, linear nearest neighbor classification, and support vector machine.

4. A diagnosis support system according to claim 1, wherein said query-by-example image retrieval application classifies said query image with labels from the cluster of the most similar representative database image as determined by said similarity measure.

5. A diagnosis support system according to claim 1, wherein said image features further comprise tissue types and diagnostic features selected from the group consisting of anatomical features, vessels, acetowhite color and opacity, lesion margins, CIN 1, CIN 2, CIN 3, CIS, and invasive carcinoma.

6. A diagnosis support system according to claim 1, wherein said clustering was performed by using a process selected from the group consisting of semi supervised learning via normalized graph cut clustering, generalized conditional random fields and hidden Markov models, to provide clusters of said database images.

7. A diagnosis support system according to claim 1, wherein meta-data is associated with at least some of said database images, wherein said meta-data includes keywords and annotations.

8. A diagnosis support system according to claim 1, wherein said cluster feature vector is computed by image features from the mean example in the cluster.

9. A diagnosis support system according to claim 1, wherein said query-by-example image retrieval application returns representative database images from the most similar cluster to said user, together with representative database images from the second best cluster for user feedback.

10. A diagnosis support system according to claim 9, wherein said user feedback includes keyword feedback relating to keywords associated with said returned representative database images and image search feedback relating to similarity of said returned representative database images.

11. A diagnosis support system according to claim 10, wherein said keyword feedback is provided by expert users who confirm or reject proposed keywords for said representative database images.

12. A diagnosis support system according to claim 10, wherein said image search feedback comprises updating search vectors of said returned representative database images based on said user'"'"'s evaluation of the relevance of said returned representative database images.

13. A diagnosis support system according to claim 1, wherein said similarity measure comprises:

a relation-based similarity measure selected from the group consisting of Dice'"'"'s coefficient, Jaccard'"'"'s similarity coefficient, normalized adjacency matrix, and multivariate similarity measures; and

a content-based similarity measure selected from the group consisting of Jaccard'"'"'s similarity coefficient, Contents similarity, Cosine Similarity Measure, Earth Mover'"'"'s Distance, Integrated Region Matching, relative differential entropy, and local interest point detectors;

wherein said relation-based similarity measure and said content-based similarity measure are combined using a method selected from the group consisting of weighted sum and learning algorithms.

14. A diagnosis support system according to claim 1, wherein:

said local features of said regions comprise color, texture and shape.

15. A diagnosis support system according to claim 1, wherein said database also contains user-defined imagery and user-defined annotations.

16. A diagnosis support system according to claim 7, further comprising:

a text search application to query said database based on text in said meta-data.

17. A diagnosis support system according to claim 1, further comprising:

a query-by-keyword image retrieval application that retrieves selected database images based on keywords associated with said selected database images.

18. A diagnosis support system according to claim 1, wherein said clustering algorithm does not specify the number of clusters in advance.

19. A diagnosis support system according to claim 1, further comprising an information center in communication with said database and said user, wherein experts can review said query image and provide a diagnosis.

20. A process for providing diagnosis support to a user, comprising:

providing a database containing high-resolution standardized database images, wherein at least some of said database images are unlabeled;

presenting a query image;

automatically computing feature vectors by image features in regions in said images by quantitatively describing image signatures for said images, by;

segmenting said images into regions; and

extracting features from said regions to produce feature vectors;

clustering said database images into clusters, each cluster having a cluster feature vector computed by image features in regions in said database images in said cluster;

retrieving database images similar to said query image by applying a similarity measure between said feature vector for said query image and said cluster feature vectors; and

returning a list of images similar to said query image, ranked by similarity.

21. A process for providing diagnosis support to a user, according to claim 20, wherein said clustering step is performed using semi supervised learning via normalized graph cut clustering to provide clusters of said database images.

22. A process for providing diagnostic support to a user, according to claim 20, wherein said returning step comprises returning a representative database image from the most similar cluster to said user, together with a representative database image from the second best cluster for user feedback.

23. A process according to claim 20, wherein said database has meta-data associated with at least some of said database images, wherein said meta-data includes keywords and annotations.