Image processing device and image processing method in image processing device for identifying features in an image

• US 8,369,626 B2
• Filed: 06/24/2011
• Issued: 02/05/2013
• Est. Priority Date: 12/28/2005
• Status: Active Grant

• Current Assignee: Olympus Corporation
• Original Assignee: Olympus Medical Systems Corporation (Olympus Corporation)
• Inventors: Nishimura, Hirokazu, Nonami, Tetsuo
• Primary Examiner(s): Lu, Tom Y
• Assistant Examiner(s): CONWAY, THOMAS A
• Application Number:
  US13/168,483
• Publication Number:
  US 20110255758A1
• Time in Patent Office: 592 Days
• Field of Search: None
• US Class Current: 382/225
• CPC Class Codes: A61B 1/00016 using wireless means
• CPC Class Codes: A61B 1/00036 Means for power saving, e.g. sleeping mode
• CPC Class Codes: A61B 1/00055 for alerting the user
• CPC Class Codes: A61B 1/041 Capsule endoscopes for imaging
• CPC Class Codes: A61B 5/073 Intestinal transmitters
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/10068 Endoscopic image
• CPC Class Codes: G06T 2207/30028 Colon; Small intestine
• CPC Class Codes: G06T 2207/30168 Image quality inspection
• CPC Class Codes: G06T 7/0002 Inspection of images, e.g. flaw detection
• CPC Class Codes: G06V 10/56 relating to colour
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A plurality of images inputted in an image signal input portion are divided into a plurality of regions by an image dividing portion, and a feature value in each of the plurality of regions is calculated by a feature value calculation portion and divided into a plurality of subsets by a subset generation portion. On the other hand, a cluster classifying portion classifies a plurality of clusters generated in a feature space into any one of a plurality of classes on the basis of the feature value and occurrence frequency of the feature value. And a classification criterion calculation portion calculates a criterion of classification for classifying images included in one subset on the basis of a distribution state of the feature value in the feature space of each of the images included in the one subset.

Claims

1. An image processing device, comprising:

an image signal input portion for inputting an image signal on the basis of a plurality of images obtained in a time series by medical equipment having an imaging function;

an image dividing portion for dividing the plurality of images into a plurality of regions, respectively, on the basis of the image signal inputted in the image signal input portion;

a feature value calculation portion for calculating one or more feature values in each of the plurality of regions divided by the image dividing portion;

an image region classifying portion for classifying each of regions into any one of a plurality of classes on the basis of the feature value;

a representative value calculation portion for calculating a representative value of the feature value of at least one class in each of the plurality of images;

a smoothing portion for applying smoothing in a time direction along the time series to each of the representative value;

a variation detection portion for detecting variation in the representative value in the time series and detecting a time zone or an image in which the variation in the representative value becomes locally large on the basis of the representative value applied with the smoothing by smoothing portion; and

a determining portion for determining that a lesion has been imaged in the time zone or the image, or determining that an imaged portion that has changed in the time zone or the image, when it is detected that the time zone or the image, in which the variation in the representative value becomes locally large, is present only in any one of the plurality of classes.

2. The image processing device according to claim 1, wherein the representative value is an average value of a feature value in each of the plurality of classes.

3. The image processing device according to claim 1, wherein, when it is detected that the time zone is present in any two classes of the plurality of classes on the basis of a detection result of the variation detection portion, the determining portion further determines that, in the time zone, a part to be imaged by the medical equipment has changed from one part corresponding to one of the classes to another part corresponding to the other of the two classes.

4. The image processing device according to claim 3, wherein the representative value is an average value of a feature value in each of the plurality of classes.

5. An image processing method in an image processing device, comprising:

an image dividing step for dividing an image into a plurality of regions, respectively, on the basis of an image signal inputted on the basis of the image obtained in a time series by medical equipment having an imaging function;

a feature value calculation step for calculating one or more feature values in each of the plurality of regions divided by the image dividing step;

an image region classifying step for classifying each of the plurality of regions to any one of a plurality of classes on the basis of the feature value;

a representative value calculation step for calculating a representative value of the feature value of at least one class in each of the plurality of images;

a smoothing step for applying smoothing in a time direction along the time series to each of the representative value;

a variation detection step for detecting variation in the representative value in the time series and detecting a time zone or an image in which the variation in the representative value becomes locally large on the basis of the representative value applied with the smoothing by smoothing portion; and

a determining step for determining that a lesion has been imaged in the time zone or the image detected by the variation detection step, or determining that an imaged portion has changed in the time zone or the image, when it is detected that the time zone or the image, in which the variation in the representative value becomes locally large, is present only in any one of the plurality of classes.

6. The image processing method according to claim 5, wherein the representative value is an average value of a feature value in each of the plurality of classes.

7. The image processing method according to claim 5, wherein, when it is detected that the time zone is present in any two classes of the plurality of classes on the basis of a detection result of the variation detection step, the determining step further determines that, in the time zone, a part to be imaged by the medical equipment has changed from one part corresponding to one of the classes to another part corresponding to the other of the two classes.

8. The image processing method according to claim 7, wherein the representative value is an average value of a feature value in each of the plurality of classes.

9. An image processing method in an image processing device comprising:

dividing an image obtained in a time series by medical equipment into a plurality of regions;

calculating one or more feature values in each of the plurality of regions;

classifying each of the plurality of regions in any one of a plurality of classes on the basis of the feature value;

calculating a representative value of the feature value of at least one class in each of the plurality of images;

applying smoothing in a time direction along the time series to each of the representative value;

detecting variation in the representative value in the time series;

detecting a time zone or an image in which the variation in the representative value becomes locally large on the basis of the representative value applied with the smoothing; and

determining that a lesion has been imaged in the time zone or the image, or determining that an imaged portion has changed in the time zone or the image, when it is detected that the time zone or the image, in which the variation in the representative value becomes locally large, is present only in any one of the plurality of classes.

10. The image processing method according to claim 9, wherein, when it is detected that the time zone is present in any two classes of the plurality of classes, it is further determined that, in the time zone, a part to be imaged by the medical equipment has changed from one part corresponding to one of the two classes to another part corresponding to the other of the two classes.