A cancer cell detection device includes a computer with a database and a display and a microscope coupled to the computer. The microscope has a base upon which a biopsy sample can be placed. The device further includes a camera coupled to the microscope and computer. The camera is configured to capture images of the biopsy sample. The device also has a filter configured to attach to the microscope and a connection feature for connecting the computer to the camera and the filter. The computer further includes a processor that processes the images captured by the camera and classifies the images according to known variables stored in the database.

Embodiments of the invention are related to a system and method of controlling a display of an image stream. The system may include a memory to store the image stream; the image stream may comprise a plurality of image frames. The system may further include a processor configured to execute the method. The moving image stream may be displayed to a user in an image stream display area of a screen and a frame rate control interface may be generated on a speed control area of the screen, such that the image stream display area is horizontally adjacent to the speed control area. An indication of a desired frame rate for displaying the image stream of the image frames may be received from the user, in that the frame rate may be selected according to a location of a pointing indicator in the speed control area.

A medical system includes a processor. The processor is configured to calculate a presence probability of a lesion in a not-yet-observed region inside a hollow organ of a patient, the not-yet-observed region being specified on the basis of an image that has been captured by an imaging sensor of an endoscope inside the hollow organ, and spatial disposition information of a distal end of an insertion part of the endoscope.

A method for measuring objects in a digestive tract based on an imaging system is provided. The imaging system captures a detection image in the measurement stage. The depth distance z.sub.i from a target point P′ to a board is calculated, a correction factor is obtained according to the predicted brightness g.sup.−1(z.sub.i) of a reference point P in the detection image. The depth image z(x, y) from the actual position of each pixel to the board is calibrated by the correction factor. The scale r of each pixel is calculated according to the depth image z(x, y). The actual two-dimensional coordinates S.sub.i′ of each pixel are calculated by the scale r, and the actual three-dimensional coordinates (S.sub.i′, z(x, y)) of each pixel are obtained. The distance between any two pixels in the detection image or the area within any range are calculated.

A luminal structure calculation apparatus includes at least one processor including hardware. The processor acquires picked-up images at a plurality of points in time including a same site of an object acquired by an image pickup unit provided in an insertion section inserted into a lumen serving as the object and three-dimensional disposition including information concerning at least a part of a position or a direction of the image pickup unit and calculates a position of the same site based on the picked-up images at the plurality of points in time and the three-dimensional disposition to calculate a three-dimensional structure of the lumen.

There is provided an image processing device comprising a processor, in which the processor acquires a plurality of endoscopic images obtained by picking up images of an observation target with an endoscope, calculates a raw score related to a determination of a severity or a stage of a disease of the observation target, on the basis of each of the endoscopic images, decides a final score on the basis of the raw score, and performs control to display the final score and/or a change over time of the final score in real time on a display.

A set of pre-operative images may be captured of an anatomical structure using an endoscopic camera. Each captured image is associated with a position and orientation of the camera at the moment of capture using image guided surgery (IGS) techniques. This image data and position data may be used to create a navigation map of captured images. During a surgical procedure on the anatomical structure, a real-time endoscopic view may be captured and displayed to a surgeon. The IGS navigation system may determine the position and orientation of the real-time image; and select an appropriate pre-operative image from the navigation map to display to the surgeon in addition to the real-time image.

Embodiments of a system, a machine-accessible storage medium, and a computer-implemented method are described in which operations are performed. The operations comprising receiving a plurality of image frames associated with a video of an endoscopy procedure, generating a probability estimate for one or more image frames included in the plurality of image frames, and identifying a transition in the video when the endoscopy procedure transitions from a first phase to a second phase based, at least in part, on the probability estimate for the one or more image frames. The probability estimate includes a first probability that one or more image frames are associated with a first phase of the endoscopy procedure.

Systems and methods for specular reflection reduction in endoscope visualizations are described. A method includes receiving an image including a region of specular reflection. The method includes detecting the region of specular reflection in the image. The method includes estimating image information for a portion of the region of specular reflection. The method also includes reconstructing the image including the image information populated into the region of specular reflection.

An electronic endoscope system includes a plotting unit which plots pixel correspondence points, which correspond to pixels that constitute an intracavitary color image that has a plurality of color components, on a target plane according to color components of the pixel correspondence points, the target plane intersecting the origin of a predetermined color space; an axis setting unit which sets a reference axis in the target plane based on pixel correspondence points plotted on the target plane; and an evaluation value calculating unit which calculates a prescribed evaluation value with respect to the captured image based on a positional relationship between the reference axis and the pixel correspondence points.