A method (400) and a system (200) for generating a chromatically modified image of one or more components on a microscopic slide (303) is disclosed. In one aspect of the invention, the method includes obtaining the image of the one or more components on the microscopic slide (303). Additionally, the method (400) includes processing the image to identify the one or more components. The method (400) further includes segmenting at least one part of the one or more components identified from the image. Furthermore, the method (400) includes chromatically modifying the at least one part of the one or more components and generating a chromatically modified image of the one or more components.

A method for analyzing an immunohistochemistry (IHC) image is provided and includes: segmenting nuclei from the IHC image according to a machine learning model; removing pixels belonging to the nuclei and pixels in a color range from the IHC image to obtain multiple cytoplasmic pixels; assign the cytoplasmic pixels to the nuclei to form multiple cells according to the locations of the cytoplasmic pixels; and calculate a pixel staining score of each pixel in the cells, thereby calculating a cell staining score for each cell.

A method of locating a defect in an e-coat on a surface can include acquiring an image of the surface. A correction coefficient can be applied to the image to form an adjusted image. The correction coefficient can relate pixel values of the image to a calibration value. The adjusted image can be separated into a spectral component which can be modified by a block average determination to create a modified spectral component. The spectral components can be compared with the modified spectral components to form a difference image. The difference image can be dilated and eroded. A region of interest can be identified from an image region using a blob detection. The defect can be classified as a defect type. The defect can be repaired or a coding parameter can be altered based on the defect.

In an embodiment, a method (100) is described. The method comprises receiving (102) data corresponding to a plurality of radiographic imaging slices of a body. The method further comprises determining (104) a position of a needle inserted in the body. The determination is based on combining information from at least one of the radiographic imaging slices comprising an indication of a first portion of the needle outside the body and at least one other of the radiographic imaging slices comprising an indication of a second portion of the needle inside the body. A combined needle region is generated by merging data corresponding to a position of the first portion of the needle outside the body with data corresponding to a position of the second portion of the needle inside the body. The method further comprises generating (106) display data for providing a visual representation of the needle in an image of the body in combination with a visual representation of at least the first and second portions of the needle superimposed on the image. The image is in a plane that is digitally tilted with respect to a plane parallel to the plurality of radiographic imaging slices.

Systems and methods for hybrid depth regularization in accordance with various embodiments of the invention are disclosed. In one embodiment of the invention, a depth sensing system comprises a plurality of cameras; a processor; and a memory containing an image processing application. The image processing application may direct the processor to obtain image data for a plurality of images from multiple viewpoints, the image data comprising a reference image and at least one alternate view image; generate a raw depth map using a first depth estimation process, and a confidence map; and generate a regularized depth map. The regularized depth map may be generated by computing a secondary depth map using a second different depth estimation process; and computing a composite depth map by selecting depth estimates from the raw depth map and the secondary depth map based on the confidence map.

A method for generating at least one image from a bird's eye view. The method includes: a) carrying out a sensor data point cloud compression; b) carrying out a point cloud filtering in a camera perspective; c) carrying out an object completion; and d) carrying out a bird's eye view segmentation and generating an elevation map.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Systems and methods are provided for three-dimensional scanning and measurement by a device having a processor. The processor is configured to receive images of an object from at least .two angles; preprocess the images using morphological refinement; create a source point cloud based on the images; remove outliers from the source point cloud; globally register the source point cloud to generate a transformed source point cloud; compare the transformed source point cloud with a target point cloud to generate a stitched point cloud that thereby creates a stitched 3D model; measure the resulting stitched 3D model; and provide the resulting stitched 3D model for comparison to a digitized item to assess sizing of the 3D model to the item.

A method, a computer program product, and a computer system for classifying bacteria. The method comprises extracting a morphology signature corresponding to one or more bacteria and extracting a motility signature corresponding to the one or more bacteria. The method further comprises merging the morphology signature and the motility signature into a merged vector signature and classifying the one or more bacteria based on the merged vector signature.

A segmentation neural network is extended to provide classification at the segment level. An input image of a document is received and processed, utilizing a segmentation neural network, to detect pixels having a signature feature type. A signature heatmap of the input image can be generated based on the pixels in the input image having the signature feature type. The segmentation neural network is extended from here to further process the signature heatmap by morphing it to include noise surrounding an object of interest. This creates a signature region that can have no defined shape or size. The morphed heatmap acts as a mask so that each signature region or object in the input image can be detected as a segment. Based on this segment-level detection, the input image is classified. The classification result can be provided as feedback to a machine learning framework to refine training.