APPARATUS AND METHOD FOR DETECTING OBJECT IN IMAGE, AND APPARATUS AND METHOD FOR COMPUTER-AIDED DIAGNOSIS

• US 20160171341A1
• Filed: 12/15/2015
• Published: 06/16/2016
• Est. Priority Date: 12/15/2014
• Status: Active Application

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: JEON, Joo Hyuk
• Granted Patent: US 10,255,673 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06T 2207/20016 Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
• CPC Class Codes: G06T 2207/20076 Probabilistic image processing
• CPC Class Codes: G06T 2207/30096 Tumor; Lesion
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/75 involving models
• CPC Class Codes: G06V 10/443 by matching or filtering
• CPC Class Codes: G06V 30/2504 Coarse or fine approaches, e.g. resolution of ambiguities or multiscale approaches

Abstract

An apparatus for detecting an object in an image, including a detection group determiner configured to classify a plurality of feature levels into one or more detection groups, wherein the plurality of feature levels includes multiple scales and are arranged as a feature pyramid; and a detection group processor configured to create one or more feature maps corresponding to the one or more detection groups, and to detect an object by applying one or more models to the one or more feature maps.

Claims

1. An apparatus for detecting an object in an image, the apparatus comprising:

a detection group determiner configured to classify a plurality of feature levels into one or more detection groups, wherein the plurality of feature levels includes multiple scales and is arranged as a feature pyramid; and

a detection group processor configured to create one or more feature maps corresponding to the one or more detection groups, and to detect an object by applying one or more models to the one or more feature maps.

2. The apparatus of claim 1, wherein the detection group determiner comprises a computational cost estimator configured to generate a plurality of computational cost estimates corresponding to the plurality of feature levels, and a detection group classifier configured to classify the plurality of feature levels into the one or more detection groups using the plurality of computational cost estimates.

3. The apparatus of claim 2, wherein the plurality of computational cost estimates comprises a first plurality of feature generation computation costs associated with feature generation during application of a scale-invariant model, a first plurality of score computation costs associated with score calculation during application of a scale-invariant model, and a second plurality of score computation costs associated with score calculation during application of a multi-scale model.

4. The apparatus of claim 3, wherein the detection group classifier is configured to compare the first plurality of feature generation costs and the first plurality of computational costs, to the second plurality of score computation costs, and classify the plurality of feature levels into the one or more detection groups based on the comparison.

5. The apparatus of claim 1, wherein the detection group processor comprises a feature map creator configured to create one or more feature maps associated with the one or more detection groups, wherein each one of the one or more feature maps is created at a scale corresponding to a lowest feature level in each one of the one or more detection groups.

6. The apparatus of claim 1, wherein the detection group processor is configured to determine a number of models to be applied the one or more detection groups based on one or more sizes associated with the one or more detection groups.

7. The apparatus of claim 6, wherein the model applier is configured to calculate one or more scores corresponding to the one or more detection groups by applying the a plurality of scaled filters to the one or more feature maps, wherein a number of the plurality of scaled filters corresponds to the number of models, and to detect an object using the one or more scores.

8. The apparatus of claim 7, wherein the model applier is configured to adjust a frequency of detection based on a size of the object to be detected and to calculate the one or more scores based on the adjusted frequency of detection.

9. A method for detecting an object in an image, comprising:

classifying a plurality of feature levels into one or more detection groups, wherein the plurality of feature levels includes multiple scales and is arranged as a feature pyramid;

creating one or more feature maps associated with the one or more detection groups; and

detecting an object by applying one or more models to the one or more feature maps.

10. The method of claim 9, wherein the classifying the plurality of feature levels into the one or more detection groups comprises generating a plurality of computational cost estimates associated with the plurality of feature levels and classifying the plurality of feature levels into the one or more detection groups using the plurality of computational cost estimates.

11. The method of claim 10, wherein the plurality of computational cost estimates include a first plurality of feature generation computation costs associated with feature generation during application of a scale-invariant model, a first plurality of score computation costs associated with score calculation during application of a scale-invariant model, and a second plurality of score computation costs associated with score calculation during application of a multi-scale model.

12. The method of claim 11, wherein the classifying the plurality of feature levels into the one or more detection groups includes comparing the first plurality of feature generation computation costs and the first plurality of score computation costs, with the second plurality of score computation costs, and the plurality of feature levels is classified into the one or more detection groups based on the comparison.

13. The method of claim 11, wherein the classifying the plurality of feature levels into the one or more detection group comprises:

creating a first detection group that includes a lowest feature level of the feature pyramid;

calculating a first computation cost by combining a first feature generation computation cost of the first plurality of feature generation computation costs with a first score computation costs of the first plurality of score computation costs;

comparing the first computation cost with a second score computation cost of the second plurality of computational costs associated with a first feature level that is higher than a highest feature level of the first detection group;

in response to the first computation cost being greater than the second score computation cost, adding the first feature level to the first detection group, and

in response to the first computation cost being less than or equal to than the second score computation cost, creating a second detection group to include the first feature level.

14. The method of claim 9, wherein the creating of the one or more feature maps includes creating a feature map at a level that corresponds to a lowest feature level in each one of the one or more detection groups.

15. The method of claim 9, wherein the detection of the object comprises determining a number of models to be applied to the one or more detection groups based on one or more sizes associated with the one or more detection group.

16. The method of claim 15, wherein the detecting the object comprises calculating one or more scores corresponding to the one or more detection groups by applying a plurality of scaled filters to the one or more feature maps, wherein a number of the plurality of scaled filters corresponds to the number of models, and detecting an object using the one or more scores.

17. The method of claim 16, wherein the detecting the object further comprises adjusting a frequency of detection based on a size of the object to be detected, and wherein the calculating the one or more scores further comprises calculating the one or more scores based on the adjusted frequency of detection.

18. A computer-aided diagnosis (CAD) apparatus comprising:

an image inputter configured to receive an input medical image;

a lesion detector configured to classify a scale-space into one or more detection groups, create one or more feature maps associated with the one or more detection groups, and detect a lesion by applying one or more models to the one or more feature map; and

a lesion determiner configured to determine whether the detected lesion is benign or malignant.

19. The CAD apparatus of claim 18, wherein, with respect to a plurality of scales in the scale-space, the lesion detector is further configured to estimate a first plurality of feature generation computation costs and a first plurality of score computation costs associated with applying a scale-invariant model, and to estimate a second plurality of score computation costs by associated with applying a multi-scale model, and to classify the plurality of scales into the one or more detection groups by taking into consideration the first plurality of feature generation computation costs, the first plurality of score computation costs, and the second plurality of score computation costs.

20. The CAD apparatus of claim 18, wherein the lesion detector is configured to determine a number of the one or more models based on one or more sizes associated with the one or more detection groups, calculate a plurality of scores associated with a plurality of locations by applying one or more filters of different scales, wherein a number of the one or more filters is equal to the number of the one or more models, and detect that a location of the plurality of location whose score estimate is greater than a designated threshold is the lesion.