A method and apparatus for aligning a two-dimensional eye image with a predefined axis by rotation at a rotation angle are disclosed, the method comprising deriving the rotation angle and a de-noised image, which minimises a cost function comprising (i) a complexity measure of the de-noised image and (ii) magnitude of a noise image obtained by rotating the first image by the rotation angle and subtracting the de-noised image. Related methods and apparatus are disclosed for aligning a plurality of images with the predefined axis before alignments in transverse and parallel directions, as well as averaging the aligned images, in further embodiments, a method and apparatus of determining angle closure are disclosed, using a database of reference eye images with and without eye closure, the method comprising obtaining a two dimensional eye image, determining respective weights for each reference images that minimise a cost function comprising the difference between the received image and sum of the weighted reference images; identifying at least one of the first and second reference images having least differences with received image and determining whether the eye exhibits eye closure based on the received image being closer to first or second weighted reference images.

A method of generating a measurement of anatomy of interest from two-dimensional imaging includes receiving two-dimensional imaging associated with anatomy of interest; detecting a plurality of anatomical features of the anatomy of interest in the two-dimensional imaging using at least one machine learning model; determining characteristics of the plurality of anatomical features based on the detection of the plurality of anatomical features; and generating at least one measurement of the anatomy of interest based on at least some of the characteristics of the plurality of anatomical features.

Disclosed herein are systems, methods, and devices for classifying ophthalmic images according to disease type, state, and stage. The disclosed invention details systems, methods, and devices to perform the aforementioned classification based on weighted-linkage of an ensemble of machine learning models. In some parts, each model is trained on a training data set and tested on a test dataset. In other parts, the models are ranked based on classification performance, and model weights are assigned based on model rank. To classify an ophthalmic image, that image is presented to each model of the ensemble for classification, yielding a probabilistic classification score—of each model. Using the model weights, a weighted-average of the individual model-generated probabilistic scores is computed and used for the classification.

Data derivable from remotely detected electromagnetic radiation (16) emitted or reflected by a subject (12) is processed. The data includes physiological information. An input signal is received and indicative entities are transmitted. The indicative entities being indicative of physiological information representative of at least one vital parameter (17; 150) in a subject (12) of interest, wherein the indicative entities are detected under consideration of at least one defined descriptive model (114) describing a relation between physical skin appearance characteristics and a corresponding representation in the input signal such that non-indicative side information represented by non-indicative entities in the input signal is substantially undetectable in a resulting transmitted signal. The at least one vital parameter (17; 150) is detected from the transmitted signal including the indicative entities. The at least one vital parameter (17; 150) is extracted under consideration of detected skin color properties representing circulatory activity.

Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound or portion thereof, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, automatically segment the pixels into wound pixels and non-wound pixels, determine one or more optically determined tissue features of the wound or portion thereof, and generate a predicted or assessed healing parameter associated with the wound or portion thereof over a predetermined time interval.

A system and method for automatic reading skin for an allergy to a substance, includes a consumer electronics device that acquires images of skin; a consumable member having a surface divided into elementary areas, each elementary area with a different possible allergenic substance; and a palette that, when applied against each elementary area brings the depositing part into contact with the skin thereby depositing the corresponding possible allergenic substance on or under the skin, where an image processing operation of the image of the skin taken by the consumer electronics device localizes the location that each depositing part has deposited the possible allergenic substance during the application and provides information in relation with a sensitivity of the skin, as indicated by a visible reaction, to the possible allergenic substance at each localized location.

A technique is disclosed for helping prevent image quality of a three-dimensional image from becoming poor due to fluctuations in the rotation speed of an imaging core. For this purpose, if data is obtained from the imaging core by moving and rotating the imaging core, a cross-sectional image is generated at each movement position. Then, a direction where a guidewire is present in each of the cross-sectional images is detected. An angular difference between the direction of the detected guidewire and a preset direction is obtained so as to rotate each of the cross-sectional images in accordance with the angular difference. Then, the cross-sectional images which are previously rotated in this way are connected to one another, thereby generating the three-dimensional image.

Provided is an apparatus for measuring the thickness, roughness, and morphology index of the mandibular cortical bone using a dental panorama image to assist in the diagnosis of osteoporosis, wherein the thickness, roughness, and morphological index of the cortical bone is measured more accurately and the diagnosis of osteoporosis can be supported more accurately. An osteoporosis diagnostic support apparatus, wherein the apparatus has a contour extraction unit adapted to extract a mandibular contour from an image of a mandibular cortical bone photographed by a photographic apparatus adapted to photograph the mandibular cortical bone and surroundings thereof, a line segment extraction unit adapted to extract line segments from the image of the mandibular cortical bone photographed by the photographic apparatus; and a cortical bone thickness calculation unit adapted to calculate a thickness of the cortical bone based on the extracted mandibular contour and line segments.

Kits, diagnostic systems and methods are provided, which measure the distribution of colors of skin features by comparison to calibrated colors which are co-imaged with the skin feature. The colors on the calibration template (calibrator) are selected to represent the expected range of feature colors under various illumination and capturing conditions. The calibrator may also comprise features with different forms and size for calibrating geometric parameters of the skin features in the captured images. Measurements may be enhanced by monitoring over time changes in the distribution of colors, by measuring two and three dimensional geometrical parameters of the skin feature and by associating the data with medical diagnostic parameters. Thus, simple means for skin diagnosis and monitoring are provided which simplify and improve current dermatologic diagnostic procedures.

Aspects of the disclosure may involve a method of generating resection plane data for use in planning an arthroplasty procedure on a patient bone. The method may include: obtaining patient data associated with at least a portion of the patient bone, the patient data captured using a medical imaging machine; generating a three-dimensional patient bone model from the patient data, the patient bone model including a polygonal surface mesh; identifying a location of a posterior point on the polygonal surface mesh; creating a three-dimensional shape centered at or near the location; identifying a most posterior vertex of all vertices of the polygonal surface mesh that may be enclosed by the three-dimensional shape; using the most posterior vertex as a factor for determining a posterior resection depth; and generating resection data using the posterior resection depth, the resection data configured to be utilized by a navigation system during the arthroplasty procedure.