A method of forming a probability map is disclosed. According to one embodiment, a method may include: (1) obtaining multiple measures of multiple imaging parameters for every stop of a moving window on an image, wherein two neighboring ones of the stops of the moving window are partially overlapped with each other; (2) obtaining first probabilities of an event for the stops of the moving window by matching the measures of the imaging parameters to a classifier; and (3) obtaining second probabilities of the event for multiple voxels of a probability map based on information associated with the first probabilities.

Systems and method are described for determining a malignancy of a nodule. A medical image of a nodule of a patient is received. A patch surrounding the nodule is identified in the medical image. A malignancy of the nodule in the patch is predicted using a trained deep image-to-image network.

The present invention relates to an image analysis method for providing information for supporting illness development prediction regarding a neoplasm in a human or animal body. The method includes receiving for the neoplasm first and second image data at a first and second moment in time, and deriving for a plurality of image features a first and a second image feature parameter value from the first and second image data. These feature parameter values being a quantitative representation of a respective image feature. Further, calculating an image feature difference value by calculating a difference between the first and second image feature parameter value, and based on a prediction model deriving a predictive value associated with the neoplasm for supporting treatment thereof. The prediction model includes a plurality of multiplier values associated with image features. For calculating the predictive value the method includes multiplying each image feature difference value with its associated multiplier value and combining the multiplied image feature difference values.

A method for analysing an image of a lesion on the skin of a subject including (a) identifying the lesion in the image by differentiating the lesion from the skin; (b) segmenting the image; and (c) selecting a feature of the image and comparing the selected feature to a library of predetermined parameters of the feature. The feature of the lesion belongs to any one selected from the group: colour, border, asymmetry and texture of the image.

A method and for quantifying a three-dimensional medical image volume are provided. An embodiment of the method includes: providing a two-dimensional representation image based on the medical image volume; defining a region of interest in the two-dimensional representation image; generating a feature signature for the region of interest; defining a plurality of two-dimensional image patches in the medical image volume; calculating, for each of the image patches, a degree of similarity between the region of interest and the respective image patch on the basis of the feature signature; visualizing the degrees of similarities.

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 lesion detecting method and a lesion detecting apparatus for breast image in a rotating manner are provided. In the method, a set of breast image in the rotating manner is obtained. The set of breast image in the rotating manner contains sub breast images. The sub breast image is reconstructed, to generate a reconstructed breast image. The reconstructed breast image is compared with the set of breast image in the rotating manner without being reconstructed. Accordingly, at least one lesion position would be confirmed according to the comparing result. Therefore, viewing a three-dimensional breast image would be convenient for medical staff, and false positive of detecting lesion would be reduced.

A method for a cloud-based feedback-driven image training and recognition includes receiving a set of expert annotations of a plurality of training images of a predetermined subject matter, wherein the expert annotations include a clinical diagnosis for each image or region of interest in an image, training one or more classification models from the set of expert annotations, testing the one or more classification models on a plurality of test images that are different from the training images, wherein each classification model yields a clinical diagnosis for each image and a confidence score for that diagnosis, and receiving expert classification result feedback regarding the clinical diagnosis for each image and a confidence score yielded by each classification model.

Computational techniques are applied to video images of polyps to extract features and patterns from different perspectives of a polyp. The extracted features and patterns are synthesized using registration techniques to remove artifacts and noise, thereby generating improved images for the polyp. The generated images of each polyp can be used for training and testing purposes, where a machine learning system separates two types of polyps.

A method for endoscopic imaging including: capturing white light images with a video endoscope under white light illumination; evaluating the captured white light images for a structure having a predefined characteristic, when the presence of the structure having the predefined characteristic is found in a white light image, setting a special light imaging mode in which a light source generates special light illumination using the at least one special light and one or more images of a video stream are captured under the special light illumination and subjected to image processing in the set special light processing mode; identifying a subregion of the at least one white light image that contains the structure with the predefined characteristic and reading out only the subregion of a CMOS image sensor associated with the video endoscope, and processing the image data read out from the subregion as one or more special light images.