A method and apparatus of detection, registration and quantification of an image. The method may include obtaining an image of a lithographically created structure, and applying a level set method to an object, representing the structure, of the image to create a mathematical representation of the structure. The method may include obtaining a first dataset representative of a reference image object of a structure at a nominal condition of a parameter, and obtaining second dataset representative of a template image object of the structure at a non-nominal condition of the parameter. The method may further include obtaining a deformation field representative of changes between the first dataset and the second dataset. The deformation field may be generated by transforming the second dataset to project the template image object onto the reference image object. A dependence relationship between the deformation field and change in the parameter may be obtained.

A level of detail-transformation adaptive enhancement method for image contrast includes: dividing a remote sensing image into a plurality images of different levels of detail, the lowest level of detail defined as L and the highest level of detail defined as H, and gradually transforming an image Image.sub.i of an arbitrary level of detail i between the image Image.sub.H of the highest level of detail H and the image Image.sub.L of the lowest level of detail L from Image.sub.L to Image.sub.H through the following equation: Image.sub.i=R.sub.iImage.sub.H+(1R.sub.i)Image.sub.L. The image Image.sub.H of the highest level of detail H is an image Image.sub.ACE produced with adaptive contrast enhancement processing, or an image produced with a contrast enhancement method such as Gaussian or histogram equalization; the image Image.sub.L of the lowest level of detail L is an image Image.sub.LCE produced by common linear contrast enhancement.

A method of segmenting an MR organ volume includes performing regional mapping on the MR organ volume using a spatial prior probability map of a location of the organ to create a regionally mapped MR organ volume, and performing boundary refinement on the regionally mapped MR organ volume using a level set framework that employs the spatial prior probability map and a propagated shape constraint to generate a segmented MR organ volume.

Image segmentation based on the combination of a deep learning network and a shape-guided deformable model is provided. In various embodiments, a time sequence of images is received. The sequence of images is provided to a convolutional network to obtain a sequence of preliminary segmentations. The sequence of preliminary segmentations labels a region of interest in each of the images of the sequence. A reference and auxiliary mask are generated from the sequence of preliminary segmentations. The reference mask corresponds to the region of interest. The auxiliary mask corresponds to areas outside the region of interest. A final segmentation corresponding to the region of interest is generated for each of the sequence of images by applying a deformable model to the composite mask with reference to the auxiliary mask.

A computer implemented image processing method is disclosed. The method comprises: obtaining microscope image data defining a microscope slide image of a haematoxylin and eosin stained tissue sample, wherein the microscope slide image data comprises a plurality of image pixels; obtaining descriptor data indicating a type of tissue from which the tissue sample originates; selecting, based on the descriptor data, an image operation configured to transform the image data; applying the selected image operation to the image data to identify a number of discrete spatial regions of the image; selecting, from a data store, a set of quantitative image metrics wherein the quantitative image metrics are selected based on the descriptor data, determining, for each discrete spatial region, a sample region data value for each of the set of quantitative image metrics based on the subset of image data associated with the or each discrete spatial region, using the descriptor data to select, from the data store, at least one comparator set of tissue model data values, wherein each comparator set is associated with a different corresponding comparator tissue structure and each comparator set comprises data values of the set of quantitative image metrics for the corresponding comparator tissue structure; comparing the sample region data value for each discrete region with the at least one comparator set; and in the event that the sample region data value for the or each discrete spatial region matches the comparator set providing a map of the image data indicating that the discrete spatial region comprises the matching comparator tissue structure.

An interactive image segmenting apparatus and method are provided. The image segmenting apparatus and corresponding method include a boundary detector, a condition generator, and a boundary modifier. The boundary detector is configured to detect a boundary from an image using an image segmentation process. The feedback receiver is configured to receive information about the detected boundary. The condition generator is configured to generate a constraint for the image segmentation process based on the information. The boundary modifier is configured to modify the detected boundary by applying the generated constraint to the image segmentation process.

The present disclosure may provide a method for segmenting an image. The method may include obtaining an image and related information. The image may include a tumor region. The method may also include determining a region of interest in the image. The region of interest may include the tumor region. The method may also include performing a first segmentation of the region of interest to obtain a first segmentation result. The first segmentation may include: determining tumor morphology relating to the tumor region; performing a second segmentation of the region of interest to obtain a second segmentation result; and optimizing, based on the tumor morphology, the second segmentation result to obtain the first segmentation result.

A method and apparatus of detection, registration and quantification of an image. The method may include obtaining an image of a lithographically created structure, and applying a level set method to an object, representing the structure, of the image to create a mathematical representation of the structure. The method may include obtaining a first dataset representative of a reference image object of a structure at a nominal condition of a parameter, and obtaining second dataset representative of a template image object of the structure at a non-nominal condition of the parameter. The method may further include obtaining a deformation field representative of changes between the first dataset and the second dataset. The deformation field may be generated by transforming the second dataset to project the template image object onto the reference image object. A dependence relationship between the deformation field and change in the parameter may be obtained.

A system includes a first sensor configured to generate images of at least a first portion of a space. A processor of the system is configured to determine a position of a possible object in the space based on generated images.

Mesh model processing techniques and innovations are described herein. A mesh processing method may include determining, on the basis of a positional relationship between a first vector on a first mesh model and at least one first spline, at least one target value corresponding to the first corner; determining the at least one target value corresponding to the first corner to be at least one respective target value corresponding to the second corner; determining a vertex coordinate of a second vertex on the basis of the at least one target value corresponding to the second corner; and determining a second mesh model at the second corner on the basis of the vertex coordinate of the second vertex. The method can ensure that a connection surface between the second corner and an adjacent wall is still perpendicular to the wall, a thickness of the wall on the second mesh model is the same as a thickness of a wall on a first mesh model, and cracks or interpenetrating may not occur between the second corner and the adjacent wall.