SYSTEM AND METHOD FOR COMPUTER AIDED DETECTION AND DIAGNOSIS FROM MULTIPLE ENERGY IMAGES

• US 20080031507A1
• Filed: 09/21/2007
• Published: 02/07/2008
• Est. Priority Date: 11/26/2002
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Sabol, John, Rader, Amber, Thomas, Carson, Uppaluri, Renuka, Avinash, Gopal, Jabri, Kadri
• Granted Patent: US 7,796,795 B2
• Time in Patent Office: Days
• US Class Current: 382/132
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/482 involving multiple energy imaging
• CPC Class Codes: A61B 6/502 for diagnosis of breast, i.e. mammography
• CPC Class Codes: A61B 6/505 for diagnosis of bone
• CPC Class Codes: G06T 11/005 Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
• CPC Class Codes: G06T 2207/30008 Bone
• CPC Class Codes: G06T 2211/408 Dual energy
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G16H 30/20 for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A method, system, and storage medium for computer aided processing of an image set includes employing a data source, the data source including an image set acquired from X-ray projection imaging, x-ray computed tomography, or x-ray tomosynthesis, defining a region of interest within one or more images from the image set, extracting feature measures from the region of interest, and reporting at least one of the feature measures on the region of interest. The method may be employed for identifying bone fractures, disease, obstruction, or any other medical condition.

Claims

1. A method for computer aided processing of dual or multiple energy images, the method comprising:

employing a data source, the data source including a dual or multiple energy image set, the image set comprising four distinct images comprising a high energy image, a low energy image, a bone image, and a soft tissue image;

defining a region of interest within an image from the dual or multiple energy image set;

extracting a set of features from the region of interest based on image attributes from all of the four distinct images of the image set, the features comprising computed features, measured features, or both; and

overlaying the extracted features on the region of interest.

2. The method of claim 1, further comprising employing a feature selection algorithm on the region of interest and classifying the region of interest.

3. The method of claim 2, further comprising incorporating prior knowledge from training for classifying the region of interest.

4. The method of claim 3, wherein incorporating prior knowledge from training includes computing features on known samples of different normal and pathological medical conditions.

5. The method of claim 4, wherein the feature selection algorithm sorts through the features of known samples, selects useful features of known samples, and discards features of known samples which do not provide useful information.

6. The method of claim 2, wherein classifying the region of interest using an optimal set of features comprises classifying one or more medical conditions.

7. The method of claim 1, wherein processing dual or multiple energy images comprises detecting and diagnosing one or more medical conditions.

8. The method of claim 1, wherein defining a region of interest comprises manually selecting a region of interest.

9. The method of claim 1, wherein defining a region of interest comprises utilizing an automated algorithm with or without user specifications input.

10. The method of claim 1, wherein the data source further includes at least one of image acquisition system information and demographic information, symptoms, and history of patient, wherein the image acquisition system information, demographic information, symptoms, and history of patient serve as feature measures in the feature extraction.

11. The method of claim 1, wherein the data source includes dual or multi-energy volumetric CT data wherein the feature extraction is performed based on volumetric image attributes.

12. The method of claim 1, wherein the data source includes dual or multi-energy X-ray tomosynthesis multi-energy data wherein the feature extraction is performed based on volumetric image attributes.

13. The method of claim 1, further comprising indicating at least one classified region using a marker on a display of each image within the dual or multiple energy image set where the at least one classified region is located.

14. The method of claim 13, further comprising displaying a single image which incorporates all markers from each image within the dual or multiple energy image set.

15. A system for computer aided processing of dual energy images, the system comprising:

a detector generating a first image representative of photons at a first energy level passing through a structure and a second image representative of photons at a second energy level passing through the structure;

a memory coupled to the detector, the memory storing the first image and the second image;

a processing circuit coupled to the memory, the processing circuit processing a dual energy image set including a bone image, a soft tissue image, a high energy image, and a low energy image;

storing the dual energy image set in the memory as a data source;

defining a region of interest within an image from the dual energy image set;

extracting a set of features from the region of interest based on image attributes from all of the four images of the image set, the features comprising computed features, measured features, or both; and

a displaying device coupled to the processing circuit, the displaying device displaying at least one feature.

16. A storage medium encoded with a machine readable computer program code, said code including instructions for causing a computer to implement a method for aiding in processing of dual or multiple energy images, the method comprising:

employing a data source, the data source including a dual or multiple energy image set the image set comprising four distinct images comprising a high energy image, a low energy image, a bone image, and a soft tissue image;

defining a region of interest within an image from the dual or multiple energy image set;

extracting a set of features from the region of interest based on image attributes from all of the four images of the image set, the features comprising computed features, measured features, or both; and

overlaying the extracted features on the region of interest.

17. The method of claim 16, further comprising employing a feature selection algorithm on the region of interest and classifying the region of interest.

18. The method of claim 17, further comprising incorporating prior knowledge from training for classifying the region of interest.

19. The method of claim 18, wherein incorporating prior knowledge from training includes computing features on known samples of different normal and pathological medical conditions.

20. The method of claim 19, wherein the feature selection algorithm sorts through the features of known samples, selects useful features of known samples, and discards features of known samples which do not provide useful information.