Computer-aided image analysis

• US 20060224539A1
• Filed: 02/06/2006
• Published: 10/05/2006
• Est. Priority Date: 05/01/1998
• Status: Active Grant

• Current Assignee: Curtis Anderson, Health Discovery Corporation, James Roberts, Joe Mckenzie, Jules B. Paderewski, Julian N. Stern, Memorial Health Systems Incorporated, Timothy P. O'Hayer
• Original Assignee: Curtis Anderson, Garry L. Carls, Glynn Bergeron, James Roberts, Joe Mckenzie, John E. Matthews, Jules B. Paderewski, Julian N. Stern, Memorial Health Systems Incorporated, Peter J. Farley, Simpson K. Russell, Timothy P. O'Hayer
• Inventors: Zhang, Hong, Carls, Garry, Barnhill, Stephen D.
• Granted Patent: US 7,383,237 B2
• Time in Patent Office: Days
• US Class Current: 706/20
• CPC Class Codes: G06F 18/2113 by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06F 18/254 of classification results, e.g. of results related to same input data
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06V 2201/03 Recognition of patterns in medical or anatomical images
• CPC Class Codes: G16H 30/40 for processing medical images, e.g. editing
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

Digitized image data are input into a processor where a detection component identifies the areas (objects) of particular interest in the image and, by segmentation, separates those objects from the background. A feature extraction component formulates numerical values relevant to the classification task from the segmented objects. Results of the preceding analysis steps are input into a trained learning machine classifier which produces an output which may consist of an index discriminating between two possible diagnoses, or some other output in the desired output format. In one embodiment, digitized image data are input into a plurality of subsystems, each subsystem having one or more support vector machines. Pre-processing may include the use of known transformations which facilitate extraction of the useful data. Each subsystem analyzes the data relevant to a different feature or characteristic found within the image. Once each subsystem completes its analysis and classification, the output for all subsystems is input into an overall support vector machine analyzer which combines the data to make a diagnosis, decision or other action which utilizes the knowledge obtained from the image.

Claims

1-54. -54. (canceled)

55. A method for automated analysis of a digitized image having a plurality of features of interest, the method comprising:

storing a plurality of detection subsystems in a memory device in communication with a processor, wherein each of the plurality of detection subsystems comprises a pre-processing component for detecting and extracting one of the features of interest within the image data, a classification component comprising at least one first-level support vector machine for classifying the feature of interest into at least one of a plurality of possible features characteristics, an output for outputting the classified feature of interest, and an overall analyzer for combining the outputs of the plurality of detection subsystems and generating an analysis of the digitized image, the overall analyzer comprising a second-level support vector machine;

receiving image data to be processed in the processor; and

processing the image data to classify the features of interest and generate an output comprising an analysis of the digitized image.

56. The method of claim 55, wherein the digitized image is a microscope image of an ex vivo sample.

57. The method of claim 55, wherein the digitized image is taken in vivo using an imaging method selected from the group consisting of X-ray, PET (positron emission tomography), MRI (magnetic resonance imaging), CT (computed tomography), SPECT (single-photon emission computed tomography), gamma camera, confocal or visual microscopy, electrical impedance imaging, and ultrasound.