An area of interest of a patient's organ may be identified based on the presence of a possible lesion during an endoscopic procedure. The location of the area of interest may then be tracked relative to the camera view being displayed to the endoscopist in real-time or near real-time during the endoscopic procedure. If the area of interest is visually marked on the display, the visual marking is moved with the area of interest as it moves within the camera view. If the area of interest moves outside the camera view, a directional indicator may be displayed to indicate the location of the area of interest relative to the camera view to assist the endoscopist in relocating the area of interest.

A medical image processing method includes: determining a target mask of a target object in a medical image and a reference mask of a reference object in the medical image, the target mask indicating a position and a boundary of the target object, and the reference mask indicating a position and a boundary of the reference object; determining a feature size of the target object based on the target mask; determining a feature size of the reference object based on the reference mask; and determining a target size of the target object based on the feature size of the reference object, a preset mapping relationship between the feature size of the reference object and a reference size, and the feature size of the target object.

An image processing device includes: a specific candidate area extracting unit configured to extract a specific candidate area that satisfies a predetermined condition from an intraluminal image captured inside a body lumen; a reference area setting unit configured to set a reference area that includes at least a part of the specific candidate area; a local area extracting unit configured to extract local areas based on the reference area; a local feature data calculator configured to calculate local feature data that is feature data of each of the local areas; a weight setting unit configured to set a weight depending on each of the local areas based on the specific candidate area; and a feature data integrating unit configured to integrate the local feature data.

An information processing apparatus (2000) detects an abnormal region (30) from a moving image frame (14). The abnormal region (30) is a region that is estimated to represent an abnormal part inside a body of a subject. The information processing apparatus (2000) generates and outputs output information based on the number of detected abnormal regions (30).

A method for detecting polyps in endoscopy images includes pruning a plurality of two dimensional digitized images received from an endoscopy apparatus to remove images that are unlikely to depict a polyp, where a plurality of candidate images remains that are likely to depict a polyp, pruning non-polyp pixels that are unlikely to be part of a polyp depiction from the candidate images, detecting polyp candidates in the pruned candidate images, extracting features from the polyp candidates, and performing a regression on the extracted features to determine whether the polyp candidate is likely to be an actual polyp.

A system and method is provided for automated polyp detection in optical colonoscopy images. The system includes an input configured to acquire a series of optical images, and a processor configured to process the optical images. Processing steps include performing a boundary classification with steps comprising locating a series of edge pixels using at least one acquired optical image, selecting an image patch around each said edge pixel, performing a classification threshold analysis on each image patch of said edge pixels using a set of determined boundary classifiers, and identifying, based on the classification threshold analysis, polyp edge pixels consistent with a polyp edge. Processing steps for the processor also include performing a vote accumulation, using the identified polyp edge pixels, to determine a polyp location. The system also includes an output configured to indicate potential polyps using the determined polyp location.

Disclosed herein are methods for identifying polyps or lesions in a colon. In some variations, computer-implemented methods for polyp detection may be used in conjunction with an endoscope system to analyze the images captured by the endoscopic system, identify any polyps and/or lesions in a visual scene captured by the endoscopic system, and provide an indication to the practitioner that a polyp and/or lesion has been detected.

A method for isolating a potential anomaly in imaging data comprising: providing a set of at least one given anomaly property representative of a given anomaly; providing a anomaly property identifier for identifying each of the at least one given anomaly property; in the imaging data, isolating a first zone having a first property and a group of at least one other zone, each of the at least one other zone having a corresponding property different than the first property; in the imaging data, and resulting from the isolation of a first zone and a group of at least one other zone, providing a transition zone selected from a group consisting of: a closed zone separating the first zone and the group of at least one other zone; and a closed zone extending in one of the first zone and the group of at least one other zone; applying the anomaly property identifier for identifying each of the at least one given anomaly property on at least the transition zone for providing a computed indication for a selected zone, the selected zone being at least the transition zone; determining if the computed indication for the selected zone is concording with each of the at least one given anomaly property; and if the computed indication for the selected zone is concording, assigning an indication of potential anomaly candidate zone to the selected zone to thereby isolate the potential anomaly.

The present technology relates to an endoscope system in which resolution and an S/N ratio are adjusted to be well-balanced depending on an imaging condition, and further capable of changing a processing load depending on the imaging condition, a method for operating the endoscope system, and a program.

From an image signal in a body cavity imaged by an endoscope apparatus, a low frequency image including a low frequency component and a high frequency image including a high frequency component are extracted. The low frequency image is reduced by a predetermined reduction ratio, and, after image quality improvement processing is performed, is enlarged by an enlargement ratio corresponding to the reduction ratio. At that time, based on condition information indicating an imaging state, when brightness at the time of imaging is sufficient and the high frequency component does not include noise components a lot, the low frequency image and the high frequency image are added to be output as an output image. In addition, based on the condition information, when the brightness at the time of imaging is not sufficient and the high frequency component includes the noise components a lot, only the low frequency image is output as the output image. The present technology can be applied to the endoscope system.

Methods and systems are provided for aggregating features in multiple video frames to enhance tissue abnormality detection algorithms, wherein a first detection algorithm identifies an abnormality and aggregates adjacent video frames to create a more complete image for analysis by an artificial intelligence detection algorithm, the aggregation occurring in real time as the medical procedure is being performed.