The present invention discloses a method for detecting skin conditions, and the method includes the steps of: capturing a skin image from a suspected subject; decomposing the skin image into an RBX image through RBX color-space transformation; and determining skin condition of the subject according to a parameter of a color model of the RBX image.

An ultrasound diagnostic apparatus of the disclosure includes a probe; an ultrasound transceiver configured to transmit an ultrasound signal to an object through the probe and receive an echo signal reflected from the object; a controller configured to obtain first data by controlling the ultrasound transceiver to transmit a first ultrasound pulse to the object and receive the echo signal reflected from the object, to obtain second data by controlling the ultrasound transceiver to repeat an operation of transmitting a second ultrasound pulse different from the first ultrasound pulse to a first position of the object and receiving the echo signal reflected from the first position of the object a plurality of times at predetermined time intervals, to obtain third data by controlling the ultrasound transceiver to repeat an operation of transmitting the second ultrasound pulse to a second position of the object and receiving the echo signal reflected from the second position of the object the plurality of times at predetermined time intervals, and to detect the micro-calcification tissues of the first position and the second position by analyzing the obtained second and third data; and a display configured to display an ultrasound image generated based on the obtained first data and an image representing micro-calcification tissues detected in the first position and the second position.

A method for ascertaining pulmonary fibrosis disease progression or treatment response includes obtaining a first set of computed tomography (CT) images of a lung and determining a first Pulmonary Surface Index (PSI) score for the lung by detecting a first actual lung boundary of the lung within the first set of CT images, determining a first approximated lung boundary within the first set of CT images, and determining the PSI score using inputs based on the first actual lung boundary and the first approximated lung boundary. The method also includes obtaining a second set of CT images of the lung and determining a second PSI score for the lung using inputs based on a second actual lung boundary and a second approximated lung boundary. The method also includes assessing pulmonary fibrosis treatment response or disease progression based on the first PSI score and the second PSI score.

The present disclosure provides monitoring system, device and computed-implemented method for monitoring pressure ulcers. The monitoring system and the device are configured to: capture at least one image corresponding to a user; retrieve the anamnesis data of the user; and determine a pressure ulcers condition result corresponding to the at least one image and the anamnesis data according a pressure ulcers prediction model.

A method of lesion monitoring includes: obtaining, at a computing device, image data and depth data depicting a lesion on a patient; detecting, at the computing device, a set of anchor points in at least one of the image data and the depth data; defining a frame of reference according to the anchor points; based on the frame of reference, detecting physical characteristics of the lesion from the image data and the depth data; and presenting the physical characteristics of the lesion.

A computer-implemented method for providing a blood flow parameter set for a vascular malformation includes receiving time-resolved image data. The image data maps a change over time in a vessel section of an examination subject. The vessel section includes the vascular malformation. A time-resolved image of the vessel section is reconstructed from the image data. The vascular malformation is segmented in the image of the vessel section. An afferent and an efferent vessel are identified at the vascular malformation based on the image of the vessel section. An average blood flow velocity parameter and a vessel cross-sectional area parameter are determined for each of the afferent and the efferent vessel. The method includes determining and providing the blood flow parameter set for the vascular malformation based on the average blood flow velocity parameters and the vessel cross-sectional area parameters.

A method for monitoring and detecting physical anomalies may include receiving, by a processor and from a camera of a brushing apparatus, an image of a body from a predetermined distance away from the body. The method may further include detecting, by the processor, an atypical contour of the image that indicates a physical anomaly. The method may further include generating, by the processor, a notification in response to detecting the atypical contour that indicates the physical anomaly.

The disclosure relates to a computer-implemented method for analyzing an image sequence of a periodic physiological activity, a system, and a medium. The method includes receiving the image sequence from an imaging device, and the image sequence has a plurality of images. The method further includes identifying at least one feature portion in a selected image, which moves responsive to the periodic physiological activity. The method also includes detecting, by a processor, the corresponding feature portions in other images of the image sequence and determining, by the processor, a phase of a the selected image in the image sequence based on the motion of the feature portion.

A method of grouping detection events in an imaging apparatus is described herein. The detection events can include primary detection events and secondary scattered events, which are frequently discarded due to the secondary scattered events, thus reducing sensitivity of the dataset for eventual image reconstruction. The method includes cell modules cascaded with identical parametrized cells, in a pipeline fashion, having the last cell in the chain circle back to the first cell. A rotating data pointer indicates the location of the first entry in the cell pipeline. The described method enables the grouping of multiple samples of detector data in real time with no loss of information, based on a time and location of the detected event. The method can be implemented in an FPGA as a hardware-based real time process.

The present disclosure describes a computer-implemented method for processing clinical three-dimensional image. The method includes training a fully supervised segmentation model using a labelled image dataset containing images for a disease at a predefined set of contrast phases or modalities, allow the segmentation model to segment images at the predefined set of contrast phases or modalities; finetuning the fully supervised segmentation model through co-heterogenous training and adversarial domain adaptation (ADA) using an unlabelled image dataset containing clinical multi-phase or multi-modality image data, to allow the segmentation model to segment images at contrast phases or modalities other than the predefined set of contrast phases or modalities; and further finetuning the fully supervised segmentation model using domain-specific pseudo labelling to identify pathological regions missed by the segmentation model.