The mesh tracking described herein involves mesh tracking on 3D face models. In contrast to existing mesh tracking algorithms which generally require user intervention and manipulation, the mesh tracking algorithm is fully automatic once a template mesh is provided. In addition, an eye and mouth boundary detection algorithm is able to better reconstruct the shape of eyes and mouths.

Smart metrology methods and apparatuses disclosed herein process images for automatic metrology of desired features. An example method at least includes extracting a region of interest from an image, the region including one or more boundaries between different sections, enhancing at least the extracted region of interest based on one or more filters, generating a multi-scale data set of the region of interest based on the enhanced region of interest, initializing a model of the region of interest; optimizing a plurality of active contours within the enhanced region of interest based on the model of the region of interest and further based on the multi-scale data set, the optimized plurality of active contours identifying the one or more boundaries within the region of interest, and performing metrology on the region of interest based on the identified boundaries.

A method (and system) of segmenting one or more histological structures in a tissue image represented by multi-parameter cellular and sub-cellular imaging data includes receiving coarsest level image data for the tissue image, wherein the coarsest level image data corresponds to a coarsest level of a multiscale representation of first data corresponding to the multi-parameter cellular and sub-cellular imaging data. The method further includes breaking the coarsest level image data into a plurality of non-overlapping superpixels, assigning each superpixel a probability of belonging to the one or more histological structures using a number of pre-trained machine learning algorithms to create a probability map, extracting an estimate of a boundary for the: one or more histological structures by applying a contour algorithm to the probability map, and using the estimate of the boundary to generate a refined boundary for the one or more histological structures.

Methods for non-invasive or minimally invasive assessment of epithelial tissue structure are disclosed. Digital imaging and processing are used to identify cell locations. More specifically, an automated algorithm that may be used to identify epithelial tissue structure, and/or to specify the coordinates/locations of cells in the epithelial tissue structure, through non-invasive or minimally invasive imaging, and use of this information to extract values of epithelial structure related parameters are disclosed.

The present disclosure relates to a half-cast mark identification and damaged flatness evaluation and classification method for blastholes in tunnel blasting, including the following steps: S1-2: photographing first and second contrast images as well as a half-cast mark image after blasting; S3-6: performing denoising, gray-scale processing and binary processing on the above images, and identifying a boundary of a half-cast mark in each of the images; S7-9: determining a flatness damage variable, a quantitative relation among an area of a half-cast mark region, the damage variable and a fractal dimension, and a damage value of the half-cast mark image; S10-11: forming five-dimensional (5D) eigenvectors to obtain multi-dimensional digital information features of the images; and S12-13: selecting eigenvectors of 60 images as training data to input to a naive Bayes classifier (NBC), and taking eigenvectors of remaining 30 images as classification data to input the above well-trained NBC for classification.

An imaging system is provided that includes a gantry defining a bore configured to accept an object to be imaged, wherein the gantry is configured to rotate about the bore. The system includes multiple detector units mounted to the gantry and configured to rotate with the gantry around the bore in rotational steps, each detector unit configured to sweep about a corresponding axis and acquire imaging information while sweeping about the corresponding axis. The system includes at least one processor operably coupled to at least one of the detector units that is configured to acquire, during an initial portion of a scan, imaging information of the object based on an initial contour and to detect an actual emission contour based on the imaging information. The processor is configured to update a scan sweep plan based on the detected actual emission contour for a remaining portion of the scan.

Provided are a virtual stent placement apparatus, a virtual stent placement method, and a virtual stent placement program that simplify an operation of virtually placing a stent in a blood vessel extracted from a medical image. An extraction unit (22) extracts a blood vessel region (30) from a three-dimensional image (V0). A display control unit (26) displays a three-dimensional image (V1) including the blood vessel region (30). The information acquisition unit (23) acquires information of the diameter of a virtual stent placed in the blood vessel region (30), a maximum contour length of the virtual stent, and a start position (S1) in a case in which the virtual stent is placed. A placement unit (24) places the virtual stent having a maximum contour length (L0) from the start position (S1) along a maximum contour line of the blood vessel region (30) in the blood vessel region (30).

Methods and products for automated real-time ovarian follicular detection, monitoring and analysis are provided. The devices and methods allow for remote or local analysis, while minimizing or eliminating the need for technician review of the output images. The methods are useful in human and non-human subjects including companion animals and other animals such as endangered species.

Surgical systems, computer-implemented methods, and software programs for producing a patient-specific virtual boundary configured to constrain movement and/or operation of a surgical tool in response to the surgical tool interacting with the patient-specific virtual boundary. The implementations include obtaining a generic virtual boundary including a generic surface with a generic edge, and positioning the generic virtual boundary relative to a virtual anatomical model such that the generic surface intersects the virtual anatomical model. The implementations include computing an intersection of the generic surface and the virtual anatomical model to define a cross-sectional contour of the virtual anatomical model, and morphing the generic edge to the cross-sectional contour to produce a customized surface with a patient-specific edge. The implementations include generating a customized face extending from, and along, the patient-specific edge, and producing the patient-specific virtual boundary by merging the customized surface and the customized face.

In a process for generating garment model data representative of a piece of garment, input image data containing a view of the piece of garment are processed. A type of wearing condition is determined as at least one of a first type of worn garment and of a second type of not-worn garment. If the first type is determined, a shape of the piece of garment and a shape of the person wearing the garment are identified utilizing an active contour modelling approach based on a preset body model. The identified shapes are adapted based on a garment template model. The garment model data are determined from the input image data based on the adapted identified shapes. If the second type is determined, a shape of the piece of garment is identified. The input image data are iteratively compared with a respective garment template model to identify at least one matching garment template model. The identified shape is aligned with a shape of the at least one matching garment template model and the garment model data are determined from the input image data based on the identified shape and on results of the aligning.