Abstract: Embodiments relate to extracting blood vessel geometry from one or more optical coherent tomography (OCT) images for use in analyzing biological structures for diagnostic and therapeutic applications for diseases that can be detected by vascular changes in the retina. An OCT image refers generally to one or more images of any dimension obtained using any one or combination of OCT techniques. Some embodiments include a method of identifying a region of interest of a retina from a plurality of retinal blood vessels in at least one optical coherence tomography (OCT) image of at least a portion of the retina. Some embodiments include a method of distinguishing between a plurality of retinal layers from vessel morphology information of retinal blood vessels in at least one optical coherence tomography (OCT) image of at least a portion of the retina.

Abstract: A method of performing a surgical procedure includes storing a software application on a memory associated with a computer, which when executed by a processor causes the software application to develop a model of a patient's anatomical structure, process images of the patient's anatomy, display the images of the patient's anatomy on a user interface associated with the computer, superimpose critical structures within the patient over the displayed images of the patient's anatomy, determine a location within the patient's body cavity where the images of the patient's anatomy were taken, and display the model of the patient's anatomical structure on the user interface, the displayed model indicating the determined location where the images of the patient's anatomy were taken.

Abstract: A method for segmenting images is provided including tessellating an image obtained from one of an image database and an imaging system into a plurality of sectors; classifying each of the plurality of sectors by applying one or more pre-defined labels to each of the plurality of sectors, wherein the pre-defined labels indicate at least one of an image quality metric (IQM) and a metric of structure; assigning each of the plurality of classified sectors an Image Quality Classification (IQC); identifying anchor sectors among the plurality of classified sectors, applying filtering and edge detection to identify target boundaries; applying contouring across contiguous sectors and using the assigned IQC as a guide to complete segmentation of an edge between any two identified anchor sectors; and smoothing across segmented regions to increase parametric second-order continuity.

Abstract: An electronic endoscope processor includes a converting means for converting each piece of pixel data that is made up of n (n?3) types of color components and constitutes a color image of a biological tissue in a body cavity into a piece of pixel data that is made up of m (m?2) types of color components, m being smaller than n; an evaluation value calculating means for calculating, for each pixel of the color image, an evaluation value related to a target illness based on the converted pieces of pixel data that are made up of m types of color components; and a lesion index calculating means for calculating a lesion index for each of a plurality of types of lesions related to the target illness based on the evaluation values calculated for the pixels of the color image.

Abstract: An information processing apparatus includes a processor configured to: acquire an image corresponding to a key character string from a target image in response to the key character string that serves as a character string specified beforehand as a key and is acquired from results of character recognition performed on the target image including character strings; extract, by using results of acquiring the image corresponding to the key character string, from the results of the character recognition a value character string that serves as a character string indicating a value corresponding to the key character string; and output the key character string and the value character string corresponding to the key character string.

Abstract: A method, apparatus, and system for generating tight two-dimensional (2D) bounding boxes for visible objects in a three-dimensional (3D) scene is disclosed. A two-dimensional (2D) segmentation image of a three-dimensional (3D) scene comprising one or more objects is generated by rendering the 3D scene with a segmentation camera. Each of the objects is rendered in a single respective different color. Next, one or more visible objects in the 3D scene are identified among the one or more objects based on the segmentation image. Next, a 2D amodal segmentation image for each of the visible objects in the 3D scene is generated separately. Each amodal segmentation image comprises only the single visible object for which it is generated. Thereafter, a 2D bounding box is generated for each of the visible objects in the 3D scene based on the amodal segmentation image for the visible object.

Abstract: Provided are an ultrasound imaging apparatus and an operation method for registering an ultrasound image and an image from another modality. The ultrasound imaging apparatus may register the ultrasound image and the image from the other modality based on a three-dimensional positional relationship between at least one external electromagnetic sensor attached to a patient's body and an ultrasound probe and on a position of a feature point extracted from the image from the other modality.

Abstract: Aspects of the disclosure provide a method for denoising an image. The method can include receiving an acquired image from an image acquisition system, and processing the acquired image with a nonlinear diffusion coefficient based filter having a diffusion coefficient that is calculated using gradient vector orientation information in the acquired image.

Abstract: Conventionally, systems and methods have been provided for manual annotation of anatomical landmarks in digital radiography (DR) images. Embodiments of the present disclosure provides system and method for anatomical landmark detection and identification from DR images containing severe skeletal deformations. More specifically, motion artefacts and exposure are filtered from an input DR image to obtain a pre-processed DR image and probable/candidate anatomical landmarks comprised therein are identified. These probable candidate anatomical landmarks are assigned a score. A subset of the candidate anatomical landmarks (CALs) is selected as accurate anatomical landmarks based on comparison of the score with a pre-defined threshold performed by a trained classifier. Position of remaining CALs may be fine-tuned for classification thereof as accurate anatomical landmarks or missing anatomical landmarks.

Abstract: The present invention relates to systems and methods for improving the accuracy of disease diagnosis and to associated diagnostic tests involving the correlation of measured analytes with binary outcomes (e.g., not-disease or disease), as well as higher-order outcomes (e.g., one of several phases of a disease). Methods of the present invention use biomarker sets, preferably those with orthogonal functionality, to obtain concentration and proximity score values for disease and non-disease states. The biomarker set's proximity scores are graphed on an orthogonal grid, with one dimension for each biomarker. The proximity scores and orthogonal gridding is then used to calculate a disease state or non-disease state diagnosis for the patient.

Abstract: A method for quantitative flow analysis of a fluid flowing in a conduit from a sequence of consecutive image frames of such a conduit, where such image frames are timely separated by a certain time interval, the method comprising: a) selecting a start image frame and an end image frame from the sequence either automatically or upon user input; b) determining a centerline of the conduit in the start image frame; c) determining a centerline of the conduit in the end image frame; d) selecting a common start point on the centerline of the start image frame and on the centerline of the end image frame either automatically or upon user input; e) selecting an end point on the centerline of the start image frame; f) selecting an end point on the centerline of the end image frame; g) calculating centerline distance between the start point and the end point of the start image frame; h) calculating centerline distance between the start point and the end point of the end image frame; and i) calculating a local flow ve

Abstract: A method includes generating a longitudinal lesion model by performing a training step on a plurality of sets of longitudinal data. Dates of medical scans of different ones of the plurality of sets of longitudinal data have relative time differences corresponding to different time spans, and each set of the plurality of sets of longitudinal data corresponds to one of a plurality of different patients. The longitudinal lesion model is utilized to perform an inference step on a received medical scan to generate, for a lesion detected in the received medical scan, a plurality of lesion change prediction data for a corresponding plurality of different projected time spans ending after the current date. At least one of the plurality of lesion change prediction data is transmitted for display.

Abstract: The ultrasonic diagnostic apparatus according to the present embodiment includes a frequency characteristic analysis circuit, a filter setting circuit, and a filter processing circuit. The frequency characteristic analysis circuit performs a frequency analysis on a first reception signal corresponding to a region of interest of each depth, and acquires a frequency characteristic of each depth. The filter setting circuit sets a reception filter of each depth based on the acquired frequency characteristic of each depth such that the acquired frequency characteristic of each depth shows a predetermined frequency characteristic. The filter processing circuit applies the set reception filter of each depth to a second reception signal corresponding to the region of interest of each depth, the second reception signal being after the first reception signal, and converts the second reception signal into a third reception signal corresponding to the region of interest of each depth.

Abstract: A process according to certain embodiments includes generating a distal femur model including an intercondylar surface model, receiving information related to user-selected points on the intercondylar surface model, generating a datum line extending between the points, generating an axis line, and determining an AP axis based upon the axis line. Generating the axis line includes performing an axis line procedure including generating a plurality of planes along the datum line, generating a plurality of contours at intersections between the intercondylar surface model and the planes, generating saddle points at local extrema of the contours, and fitting the axis line to the saddle points. The process may further include generating an updated datum line based upon the axis line, and performing a subsequent iteration of the axis line procedure using the updated datum line.

Abstract: An object volume acquisition method of an ultrasonic image, for a probe of an ultrasonic system is disclosed.

Abstract: A method for gating in tomographic imaging system includes steps of: (a) performing a tomographic imaging on an object with a target moving periodically along a first axis for acquiring projection images; (b) obtaining projected curves by summing up pixel values along a direction of a second axis perpendicular to the first axis in each projection image; (c) determining a target zone on the projection images, wherein a central position on the first axis of the target zone is corresponding to a position having the largest variation in the projected curves on the first axis; (d) calculating parameter values of pixel values in the target zones and obtaining a curve of a moving cycle of the target according to the parameter values; and (e) selecting the projection images under the same state in the moving cycle for image reconstruction according to the curve of the moving cycle of the target.

Abstract: The present invention is enclosed in the area of machine learning, in particular machine learning for the analysis of High or Super-resolution spectroscopic data, which typically comprises analysis of highly complex samples/mixtures of substances and/or data with low resolution, for instance Laser-Induced Breakdown Spectroscopy (LIBS). It is an object of the present invention a method of computational self-learning for characterization of one or more constituents in a sample, from electromagnetic spectral information of such sample, which changes the paradigm associated with prior art methods, by using only sub-optical spectral information, i.e., obtaining the resolution of the spectral information and thereby be able to extract spectral lines—thus determining a spectral line position—from such spectral information, hence avoiding all the uncertainty associated with pixel based methods. It is also an object of the present invention a computational apparatus configured to implement such method.

Abstract: A method is described that can be used in a radar system. In accordance with one exemplary embodiment, the method includes calculating a first spectrum, which represents a spectrum of a segment of a complex baseband signal. The segment is assignable to a specific chirp of a chirp sequence contained in a first RF radar signal. The method further includes estimating a second spectrum, which represents a spectrum of an interference signal contained in the complex baseband signal, based on a portion of the first spectrum that is assigned to negative frequencies.

Abstract: A medical imaging apparatus includes processing circuitry configured to acquire a volumetric data set, acquire location information for a layer that is part of a structure represented in the volumetric data set, and perform a ray casting algorithm to generate a rendered image from the volumetric data set. The ray casting algorithm includes, for each of a plurality of rays cast through a volume of the volume of the volumetric data set: determining data values for a plurality of sample positions along the ray; estimating, based on the location information, a position of an intersection point at which the ray intersects the layer; and using a precomputed function to determine a contribution of the intersection point to an aggregated color for the ray.

Abstract: An apparatus for rendering a three-dimensional (3D) polygon mesh includes a processor, and a memory storing instructions, wherein the processor is configured to execute the instructions to obtain a plurality of multi-view images corresponding to a plurality of captured images obtained by capturing an object at different capture camera positions, obtain a 3D polygon mesh for the object, select one or more of the plurality of multi-view images as one or more texture images based on a render camera position and capture camera positions of the plurality of multi-view images, and render the 3D polygon mesh as a two-dimensional (2D) image based on the selected one or more texture images.

Abstract: The disclosure herein provides methods, systems, and devices for virtually staining biological tissue for enhanced visualization without use of an actual dye or tag by detecting how each pixel of an unstained tissue image changes in waveform after staining with a certain dye(s) and/or tag(s) or other transformation under a certain electromagnetic radiation source, developing a virtual staining transform based on such detection, and applying such virtual staining transform to an unstained biological tissue to virtually stain the tissue.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate a transformation of a model of an entity by a model of a plurality of entities are provided. According to an embodiment, a computer-implemented method can comprise identifying a plurality of parameters of a model of a plurality of entities; generating a model of an entity based on collected data of an operation of the entity, wherein the model of the entity comprises a subset of the plurality of parameters; and transforming the model of the entity based the model of the plurality of entities such that a first result from the model of the plurality of entities and a second result from the model of the entity have a relationship that satisfies a defined criterion, given same values used for the subset of the plurality of parameters.

Abstract: A system and method is disclosed for displaying augmented image data for invasive medical devices. A current orientation and a current position of the invasive medical device within a patient can be determined by applying a trained model of the invasive medical device to unannotated images of the invasive medical device as captured by an imaging device. The images of the invasive medical device can be displayed and overlaid with the current orientation and current position of the invasive medical device. User input can be received to initialize tracking of an orientation and a position of the invasive medical device as the invasive medical device is moved within the patient.

Abstract: A computer-implemented method for evaluating colonoscopy performance includes: (S1) splitting a video acquired during a colonoscopy examination into a plurality of colonoscopy images; (S2) assigning each of the colonoscopy images into a fold-inspection group or a non-fold-inspection group according to a first classification criterion and a second classification criterion, wherein the first classification criterion comprises at least one of clarity, exposure, level of tissue wrinkling, and level of occlusion in each of the colonoscopy images; and the second classification criterion comprises at least one of an amount of haustrum, an amount of colonic lumen, and a position of the colonic lumen in each of the colonoscopy images; and (S3) determining a performance rating of the colonoscopy examination according to an elapsed time of the fold-inspection group.

Abstract: A neural network based corrector for photon counting detectors is described. A method for photon count correction includes receiving, by a trained artificial neural network (ANN), a detected photon count from a photon counting detector. The detected photon count corresponds to an attenuated energy spectrum. The attenuated energy spectrum is related to characteristics of an imaging object and is based, at least in part, on an incident energy spectrum. The method further includes correcting, by the trained ANN, the detected photon count to produce a corrected photon count. The method may include reconstructing, by image reconstruction circuitry, an image based, at least in part, on the corrected photon count.

Abstract: A lesion detection ensemble machine learning model architecture comprising a plurality of trained machine learning (ML) computer models is provided. A first decoder of a lesion detection ML model processes a medical image input to generate a first lesion mapping prediction. A second decoder of the lesion detection ML model processes the medical image input to generate a second lesion mapping prediction. Combinational logic combines the first and second lesion mapping predictions to generate a combined prediction. Final lesion mapping output logic generates a final lesion prediction based on the combined lesion mapping prediction. The final lesion mapping output logic outputs the final lesion prediction for further downstream computing operations. The first decoder is trained with a first loss function that is configured to counterbalance a training of the second decoder that is trained using a second loss function different from the first loss function.

Abstract: Solutions for more efficient and effective optical character recognition with respect to an input text segment are disclosed. In one example, a method includes processing an input text image using a deep character overlap detection machine learning model in order to generate a character map for the input text image, an overlap map for the input text image, and an affinity map for the input text image; generating an overlap-aware word boundary recognition output based at least in part on the character map, the overlap map, and the affinity map, wherein the overlap-aware word boundary recognition output describes one or more inferred word regions of the input text image; and performing one or more prediction-based actions based at least in part on the overlap-aware word boundary recognition output.

Abstract: An x-ray image orientation detection and correction system including a detection and correction computing device is provided. The processor of the computing device is programmed to execute a neural network model that is trained with training x-ray images as inputs and observed x-ray images as outputs. The observed x-ray images are the training x-ray images adjusted to have a reference orientation. The processor is further programmed to receive an unclassified x-ray image, analyze the unclassified x-ray image using the neural network model, and assign an orientation class to the unclassified x-ray image. If the assigned orientation class is not the reference orientation, the processor is programmed to adjust an orientation of the unclassified x-ray image using the neural network model, and output a corrected x-ray image. If the assigned orientation class is the reference orientation, the processor is programmed to output the unclassified x-ray image.

Abstract: Disclosed is a computer-implemented method of determining an assignment of an object acquire patient image data of interest recognizable in a digital medical patient image such as a tumour or other medical anomaly such as an implant to an anatomical region. The medical patient image is registered with atlas data, The assignment is then determined by calculating a score value defining an amount of volume intersection between the object of interest and a digital object defining a specific anatomic region, for example a bounding box around a specific organ, which is defined in the atlas data.

Abstract: A computer implemented method of generating at least one shape of a region of interest in a digital image is provided.

Abstract: Methods and systems are presented to analyze a retinal image of an eye and assigns features to known anatomical structures such as retinal layers. One example method includes receiving interferometric image data of an eye. A set of features is identified in the image data. A first subset of identified features is associated with known retinal structures using prior knowledge. A first set of characteristic metrics is determined of the first subset of features. A second set of characteristic metrics is determined of a second subset of features. Using the characteristic metrics of the first and the second sets, the second subset of features is associated with the retinal structures. Another example method includes dividing interferometric image data into patches. The image data in each patch is segmented to identify one or more layer boundaries. The segmentation results from each patch are stitched together into a single segmentation dataset.

Abstract: Systems and methods for automated assessment of a vessel are provided. One or more input medical images of a vessel of a patient are received. A plurality of vessel assessment tasks for assessing the vessel is performed using a machine learning based model trained using multi-task learning. The plurality of vessel assessment tasks are performed by the machine learning based model based on shared features extracted from the one or more input medical images. Results of the plurality of vessel assessment tasks or a combination of the results of the plurality of vessel assessment tasks are output.

Abstract: A system and method is disclosed for augmenting image data of an invasive medical device using optical imaging. An optical imaging sensor, separate from the invasive medical device, can generate images of the medical device within a patient. A trained model for the invasive medical device can be trained on annotated images of the invasive medical device with orientation and distance information of the invasive medical device. An imaging computer system can apply the trained model to images of the invasive medical device within the patient to determine a current orientation and a current distance of the invasive medical device. The images of the invasive medical device as captured by the optical imaging sensor, visual orientation information representing the current orientation of the invasive medical device, and visual distance information representing the current distance of the invasive medical device within the patient can be displayed.

Abstract: An image diagnosis assisting apparatus according to the present invention executes: processing of inputting an image of a tissue or cell; processing of extracting a feature amount of a tissue or cell from a processing target image; processing of extracting a feature amount of a tissue or cell from an image having a component different from that of the target image; and processing of determining presence or absence of a lesion and lesion probability for each of the target images by using a plurality of the feature amounts.

Abstract: An automated segmentation system for medical imaging data segments data into muscle and fat volumes, and separates muscle volumes into discrete muscle group volumes using a plurality of models of the medical imaging data, and wherein the medical imaging data includes data from a plurality of imaging modalities.

Abstract: Methods and systems are provided for synchronizing image capture at a multi-detector imaging system. In one example, a method includes coordinating cycling of each microscope assembly of the multi-detector imaging system through a selection of illumination channels, each microscope assembly configured to obtain an image of a portion of one of more than one microplate wells simultaneously, to generate complete images of the more than one microplate wells concurrently.

Abstract: A method comprises obtaining reference data about a 3D calibration object, the reference data comprising known coordinates for a plurality of points on the object, and obtaining measurement data comprising measurements for the plurality of points on the object, the measurement data having been generated based on scanning of the object by an uncalibrated intraoral scanner. The method comprises comparing the measurement data to the reference data to determine differences therebetween and applying the determined differences between the measurement data and the reference data for the plurality of points to a function to generate a compensation model that compensates for one or more inaccuracies of the intraoral scanner. The compensation model is then stored, wherein the compensation model causes the intraoral scanner to be a calibrated intraoral scanner and is usable to correct measurement errors of the intraoral scanner caused by the one or more inaccuracies of the intraoral scanner.

Abstract: Embodiments provide slide navigation technology that addresses challenges in digital pathology of navigating and viewing high resolution slide images. Example systems comprise a virtual slide stage (VSS) having at least one sensor that detects user movement of a target placed on the VSS, and an input component, coupled to the VSS, which provides quick function movement control of the target via quick functions. The systems also comprise a connector component that connects the VSS to a user device and transmits output from the at least one sensor and input component to the user device. The systems further comprise a computer processor, in communication with the VSS, which processes the output using a computational model to generate data representing movement profiles of the target. The computer processor executes a software component, causing the output, translated based on the movement profiles, to be relayed via a viewing application on the user device.

Abstract: A label generating system operates to generate an artificial intelligence model by: training on a training data set that includes the plurality of medical scans with the corresponding global labels; generating testing global probability data by performing an inference function that utilizes the artificial intelligence model on the plurality of medical scans with the corresponding global labels, wherein the testing global probability data indicates a testing set of global probability values corresponding to the set of abnormality classes, and wherein each of the testing set of global probability values indicates a probability that a corresponding one of the set of abnormality classes is present in each of the plurality of medical scans with the corresponding global labels; comparing the testing set of global probability values to a corresponding confidence threshold for each of the plurality of medical scans selected based on the corresponding one of the global labels; generating an updated training data set b

Abstract: An example system includes a thermal camera, a memory, and processing circuitry coupled to the thermal camera and the memory. The processing circuitry is configured to acquire a core temperature of a patient and acquire a first thermal image associated with the patient. The processing circuitry is configured to determine, based on the first thermal image, a first sensed temperature of a location associated with the patient. The processing circuitry is configured to determine a core temperature delta between the core temperature and the first sensed temperature. The processing circuitry is configured to acquire a second thermal image associated with the patient. The processing circuitry is configured to determine, based on the second thermal image, a second sensed temperature. The processing circuitry is configured to determine, based on the second sensed temperature and the core temperature delta, a measure of the core temperature.

Abstract: An apparatus for classifying a contrast level of an image is provided. One or more processors execute instructions stored in one or more memory devices which configure the one or more processors to obtain an image from an image source, extract intensity values for each pixel of the obtained image, calculate a probability distribution for the obtained image representing a number of pixels at each unique pixel value, determine, from the calculated probability distribution, a spread value representing a series pixel values including at least a predetermined number of pixels at each pixel value in the series of pixel values and classify the obtained image a member of one of three classes based on the calculated probability distribution and the determined spread value.

Abstract: Some embodiments of the disclosure provide an artificial neural network system for recognizing a lesion in a fundus image. The system includes a pre-processing module configured to pre-process a target fundus image and a reference fundus image taken from one person separately, a first neural network (12) configured to generate a first advanced feature set from the target fundus image, a second neural network (22) configured to generate a second advanced feature set from the reference fundus image, a feature combination module (13) configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set, and a third neural network (14) configured to generate, according to the feature combination set, a judgmental result of lesions.

Abstract: There is provided a system for computing connectivity of medical imaging network nodes, comprising: at least one hardware processor executing a code for: monitoring packets transmitted over a network connecting a plurality of network nodes, analyzing the monitored packets to identify packets associated with a Digital Imaging and Communications in Medicine (DICOM) protocol, analyzing the packets associated with the DICOM protocol to identify medical imaging network nodes implementing the DICOM protocol, designating a respective medical imaging type for each node of the medical imaging network nodes, and computing a data structure storing connectivity of the medical imaging network nodes each designated with the respective medical imaging type.

Abstract: An apparatus for assessing a patient's vasculature and a corresponding method identify the bifurcations in a vessel of interest on the basis of a local change in at least one geometric parameter value of the vessel of interest and adjust the fluid dynamics inside the vessel of interest to take account for said bifurcations.

Abstract: A system and method for managing sensors including determining health operation states of the sensors correlative with sensor accuracy, classifying the sensors by their respective health operation state, and teaming two sensors each having a health operation state that is intermediate to give a team having a health operation state that is healthy. The sampling frequency of the sensors to determine sensor accuracy may be dynamic.

Abstract: Disclosed is a method, a computer readable storage medium and an apparatus for processing medical image data. Input medical image data is received at a data processing system, which is an artificial intelligence-based system. An identification process is performed at the data processing system on the input medical image data to identify a volume of interest within which an instance of a predetermined anatomical structure is located. First and second determination processes are performed at the data processing system to determine, respectively, first and second anatomical directions for the instance of the anatomical structure that are defined relative to the coordinate system of the input medical image data. Output data relating to the first and second anatomical directions is output from the data processing system.

Abstract: Seismic image processing including filtering a three-dimensional (3D) seismic image for random noise attenuation via multiple processors. The filtering includes receiving a 3D image cube of seismic image data, decomposing the 3D image cube into 3D sub-cubes for parallel computation on the multiple processors, designing and applying a two-dimensional (2D) adaptive filter for image points on 2D image slices of the 3D sub-cubes via the multiple processors to give filtered 3D sub-cubes, and summing the filtered 3D sub-cubes to give a filtered 3D image cube.

Abstract: A system and method for augmenting unlabeled imaging data of invasive medical devices is disclosed. An imaging device can generate images of the invasive medical device inside a patient. A trained model for the invasive medical device can be trained on annotated images of the invasive medical device having labels indicating orientation and position information. An imaging computer system can apply the trained model to unlabeled images of the invasive medical device within the patient to determine a current orientation and a current position of the invasive medical device inside the patient. The images of the invasive medical device, visual orientation information representing the current orientation of the invasive medical device, and visual position information representing the current position of the invasive medical device inside the patient can be outputted to a display.

Abstract: A system and method for displaying and navigating breast tissue is configured for or includes obtaining a plurality of 2D and/or 3D images of a patient's breast; generating a synthesized 2D image of the breast from the obtained images; displaying the synthesized 2D image; receiving a user command, or otherwise detecting through a user interface, a user selection or other indication of an object or region in the synthesized 2D image; and displaying at least a portion of one or more images from the plurality, including a source image and/or most similar representation of the user selected or indicated object or region.

Abstract: Methods for measuring and verifying drug portions while avoiding the need to teach verification devices is provided. Characteristic key data of a drug type are compiled with a calibrated camera of a measuring device, stored in a storage device, and retrieved from or transmitted to a verification device. Individual key data for a drug portion to be verified are compiled using a calibrated camera of the verification device and compared with the characteristic key data. A digital image of a drug portion to be verified is generated with a calibrated camera and transferred to a control unit. The control unit determines individual key data for the drug portion to be verified using known calibration parameters of the camera and the digital image, compares the individual key data with characteristic key data of the corresponding drug type and verifies or rejects the drug portion based on the results of the comparison. Systems and computer readable medium are also provided.