Abstract: The invention provides methods and apparatus for image processing that perform image segmentation on data sets in two- and/or three-dimensions so as to resolve structures that have the same or similar grey values (and that would otherwise render with the same or similar intensity values) and that, thereby, facilitate visualization and processing of those data sets.

Abstract: A server includes a control unit. The control unit determines whether a parked vehicle is used as a shelter based on a first image which is included in an image captured by a camera and which is obtained by imaging the vehicle and surroundings of the vehicle.

Abstract: An information processing method includes: obtaining sensing data; determining a synthesis region in the sensing data in which recognition target data is to be synthesized with the sensing data; generating composite data by synthesizing the recognition target data into the synthesis region, the recognition target data having same or similar characteristics perceived by a human sensory system as the sensing data; obtaining recognition result data by providing the composite data to a model which has been trained using machine learning to recognize a recognition target; making a second determination based on the recognition result data and reference data including at least the synthesis region, the second determination being to determine whether to make a first determination, the first determination being to determine training data for the model based on the composite data; and making the first determination when it is determined in the second determination to make the first determination.

Abstract: Various methods for reducing artifacts in OCT images of an eye are described. In one exemplary method, three dimensional OCT image data of the eye is collected. Motion contrast information is calculated in the OCT image data. A first image and a second image are created from the motion contrast information. The first and the second images depict vasculature information regarding one or more upper portions and one or more deeper portions, respectively. The second image contains artifacts. Using an inverse calculation, a third image is determined that can be mixed with the first image to generate the second image. The third image depicts vasculature regarding the same one or more deeper portions as the second image but has reduced artifacts. A depth dependent correction method is also described that can be used in combination with the inverse problem based method to further reduce artifacts in OCT angiography images.

Abstract: An image processing device includes a detector, a setting unit, a calculator, an estimator, and a determining unit. The detector sets a three-dimensional object region by detecting a three-dimensional object. The setting unit sets a first image region corresponding to the three-dimensional object region, a second image region including a left end of and partly overlapping the first image region, and a third image region including a right end of and partly overlapping the first image region. The calculator calculates representative values of parallax-related values in pixel columns in the image regions, and calculates an approximate line of the representative values in each of the image regions. The estimator estimates a continuous structure degree, from a slope value of the approximate line of each of the image regions. The determining unit determines whether the three-dimensional object is a vehicle, from the continuous structure degree.

Abstract: A weakly supervised image semantic segmentation method based on an intra-class discriminator includes: constructing two levels of intra-class discriminators for each image-level class to determine whether pixels belonging to the image class belong to a target foreground or a background, and using weakly supervised data for training; generating a pixel-level image class label based on the two levels of intra-class discriminators, and generating and outputting a semantic segmentation result; and further training an image semantic segmentation module or network by using the label to obtain a final semantic segmentation model for an unlabeled input image. By means of the new method, intra-class image information implied in a feature code is fully mined, foreground and background pixels are accurately distinguished, and performance of a weakly supervised semantic segmentation model is significantly improved under the condition of only relying on an image-level annotation.

Abstract: Provided is a method for generating an image label, including: acquiring a partial image of a target image after acquiring the target image with a label to be generated; then, acquiring a plurality of features based on the target image and the partial image, wherein the plurality of features include a first feature of the target image and a second feature of the partial image; and finally, generating a first-type image label of the target image based on the first feature and the second feature.

Abstract: Image rotation in an endoscopic hyperspectral imaging system is described. A system includes an emitter for emitting pulses of electromagnetic radiation and an image sensor comprising a pixel array for sensing reflected electromagnetic radiation. The system includes a rotation sensor for detecting an angle of rotation of a lumen relative to a handpiece of an endoscope. The system is such that at least a portion of the pulses of electromagnetic radiation emitted by the emitter comprises electromagnetic radiation having a wavelength from about 513 nm to about 545 nm, from about 565 nm to about 585 nm, or from about 900 nm to about 1000 nm.

Abstract: A computer model is trained to classify regions of a space (e.g., a pixel of an image or a voxel of a point cloud) according to a multi-label classification. To improve the model's accuracy, the model's self-confidence is determined with respect to its own predictions of regions in a training space. The self-confidence is determined based on the class predictions, such as a difference between the highest-predicted class and a second-highest-predicted class. When these are similar, it may reflect areas for potential improvement by focusing training on these low-confidence areas. Additional training may be performed by including modified training data in subsequent training iterations that focuses on low-confidence areas. As another example, additional training may be performed using the self-confidence to modify a classification loss used to refine parameters of the model.

Abstract: A system for identifying melanoma and other skin cancer in a dermoscopy image comprises: an image analyzer having at least one processor that instantiates at least one component stored in a memory, the at least one component comprising: a segmenter configured to segment a lesion from the rest of the image, a handcrafted feature component including: a median color splitting model for separating the image into a plurality of color regions, a vessel detection model for detecting elevated vascularity, an atypical pigment network detection model for identifying a pigment network whose structure varies in size and shape, a salient point detection model for detecting salient points based on an intensity plane of the image, a color detection model for detecting at least one of a white area, a pink shade, a pink blush, and a semi-translucency, a hair detection model for characterizing detected hairs and ruler marks, an outside model that finds the above model features on non-dark-corner areas outside the segmented are

Abstract: A data partitioning system receives an input dataset for e-commerce products, each sample containing attributes and associated values for each product including at least an image; represents each sample as a node on a graph to provide a graph of nodes for the dataset; measures a relative similarity distance between each pair of nodes based on comparing at least image values for the attributes; determines for each pair of nodes whether they are related if the similarity distance between them is below a defined threshold, and if related, generate an edge between them on the graph; group the connected nodes into a first or a second group such that the grouped nodes have no edges connecting them to nodes in the other group and have a shortest relative similarity distance with each other. The groups are used as training dataset and testing data sets for a supervised machine learning classifier.

Abstract: A method and system perform single phase and multi-phase contour refinement of lesions. The method includes receiving a three dimensional input mask; receiving input slices from the medical images including a lesion; cropping the input slices with the input mask; performing lesion contour refinement for the cropped input slices and the input mask to obtain a predicted mask; and storing the predicted mask that includes 3D lesion contour refinement. A multiphase method includes deforming the 3D input mask from the reference phase to a target phase or warping the input slices from the target phase to the reference phase before contour refinement. The warped images generate an output mask in the reference phase coordinate system that is then deformed to the target phase coordinate system for display.

Abstract: A parking space recognition system includes an external environment information acquiring device and a parking space candidate detecting device. The parking space candidate detecting device includes: a virtual line calculating unit configured to calculate a virtual line connecting road side ends of parking space lines adjacent to each other; an angle calculating unit configured to calculate an angle between the virtual line and one of the parking space lines; a parking type determining unit configured to determine a parking type; and a parking space candidate setting unit configured to set at least one provisional parking space in an area between the parking space lines based on positions of the parking space lines, and to set an available parking area among the provisional parking space to the parking space candidate.

Abstract: Disclosed herein are a system and method for assisting the verification of the contouring of medical images. A system for assisting the verification of the contouring of medical images according to an embodiment of the present invention receives a medical image set including a plurality of medical images via the communication interface, receives the result information of contouring performed on a plurality of regions of interest (ROIs) on the plurality of medical images in the medical image set via the communication interface, calculates the feature value of each of the plurality of ROIs, corresponding to the result information of the contouring, for each of the plurality of medical images, and displays the boundary of each of the plurality of ROIs corresponding to the result information of the contouring by projecting the boundary onto a reference plane.

Abstract: A method for detecting and dimensioning a target object includes: obtaining depth data representing a target object; defining a mask having a structure which decreases in density away from a central point of the mask; overlaying the mask on the depth data and selecting a subset of the depth data comprising data points which contact the mask; detecting a cluster of data points from the subset; detecting, based on the cluster, the target object; and outputting a representation of the target object.

Abstract: To obtain a predictive model that shows a diagnostic prediction result with higher accuracy and high medical validity. A medical imaging apparatus includes an imaging unit that collects an image signal of an inspection target, and an image processing unit that generates first image data from the image signal and performs image processing of the first image data. The image processing unit includes a feature quantity extraction unit that extracts a first feature quantity from the first image data, a feature quantity abstraction unit that abstracts the first feature quantity to extract a second feature quantity, a feature quantity conversion unit that converts the second feature quantity into a third feature quantity extracted by second image data, and an identification unit that uses the converted third feature quantity to calculate a predetermined parameter value.

Abstract: According to one embodiment, a task assignment supporting apparatus includes processing circuitry. The processing circuitry manages types of image reading preferences indicating types of image reading tasks preferred by each radiologist. The processing circuitry acquires an image reading order and a type of image reading order indicating a type of image reading task required for the image reading order. The processing circuitry assigns to the image reading order a radiologist with a type of image reading preference corresponding to the type of image reading order.

Abstract: A data processing system for generating a fingerprint of an image implements obtaining a first digital image; determining a first fingerprint for the first digital image comprising a first coarse-grain fingerprint element and a first fine-grain fingerprint element by determining the first coarse-grain component based on the first digital image; determining the first fine-grain component based on the first digital image; and generating the first fingerprint by combining the first coarse-grain component with the first-fine grain component; obtaining a second fingerprint for a second digital image, the second fingerprint comprising a second coarse-grain component and a second fine-grain component; comparing the first fingerprint with the second fingerprint to determine a similarity score; and performing one or more actions responsive to the similarity score equaling or exceeding a similarity threshold.

Abstract: A computer-implemented method for correcting artifacts within CT images of a subject includes providing iteratively correcting a CT volumetric image with a high-contrast mask by overpainting the high-contrast mask within CT volumetric image set by evaluating a model function and updating the CT projections to reflect the overpainting using a gradient descent based on previous overpainted CT projections, back-projecting the corrected CT projections to produce a corrected volumetric image set, and comparing the corrected CT volumetric image set against a previous corrected CT volumetric image set until convergence. The original voxels overpainted by the high-contrast mask are re-inserted into the converged CT volumetric image to produce the final CT volumetric image set.

Abstract: Devices, systems, and methods obtain scan data that were generated by scanning a scanned region, wherein the scan data include groups of scan data that were captured at respective angles; generate partial reconstructions of at least a part of the scanned region, wherein each partial reconstruction of the partial reconstructions is generated based on a respective one or more groups of the groups of scan data, and wherein a collective scanning range of the respective one or more groups is less than the angular scanning range; input the partial reconstructions into a machine-learning model, which generates one or more motion-compensated reconstructions of the at least part of the scanned region based on the partial reconstructions; calculate a respective edge entropy of each of the one or more motion-compensated reconstructions of the at least part of the scanned region; and adjust the machine-learning model based on the respective edge entropies.