Abstract:
Systems and methods for long working distance optical coherence tomography (OCT). According to an aspect, an OCT system includes a reference arm. Further, the OCT system includes a sample arm operably connected to the reference arm. The sample arm includes a scanner configured to scan an optical beam. The sample arm also includes an objective positioned a predetermined distance from the scanner, configured to receive the optical beam, and to direct the optical beam to an object positioned at about the predetermined distance from the scanner for imaging of the object.
Abstract:
Disclosed herein are systems and method for segmentation and identification of structured features in images. According to an aspect, a method may include representing an image as a graph of nodes connected together by edges. For example, the image may be an ocular image showing layered structures or other features of the retina. The method may also include adding, to the graph, nodes adjacent to nodes along first and second sides of the graph. The added nodes may have edge weights less than the nodes along the first and second sides of the graph. Further, the method may include assigning start and end points to any of the added nodes along the first and second sides, respectively. The method may also include graph cutting between the start and end points for identifying a feature in the image.
Abstract:
Disclosed herein are systems and method for segmentation and identification of structured features in images. According to an aspect, a method may include representing an image as a graph of nodes connected together by edges. For example, the image may be an ocular image showing layered structures or other features of the retina. The method may also include adding, to the graph, nodes adjacent to nodes along first and second sides of the graph. The added nodes may have edge weights less than the nodes along the first and second sides of the graph. Further, the method may include assigning start and end points to any of the added nodes along the first and second sides, respectively. The method may also include graph cutting between the start and end points for identifying a feature in the image.
Abstract:
Systems and methods for long working distance optical coherence tomography (OCT). According to an aspect, an OCT system includes a reference arm. Further, the OCT system includes a sample arm operably connected to the reference arm. The sample arm includes a scanner configured to scan an optical beam. The sample arm also includes an objective positioned a predetermined distance from the scanner, configured to receive the optical beam, and to direct the optical beam to an object positioned at about the predetermined distance from the scanner for imaging of the object.
Abstract:
Disclosed herein are systems and method for segmentation and identification of structured features in images. According to an aspect, a method may include representing an image as a graph of nodes connected together by edges. For example, the image may be an ocular image showing layered structures or other features of the retina. The method may also include adding, to the graph, nodes adjacent to nodes along first and second sides of the graph. The added nodes may have edge weights less than the nodes along the first and second sides of the graph. Further, the method may include assigning start and end points to any of the added nodes along the first and second sides, respectively. The method may also include graph cutting between the start and end points for identifying a feature in the image.
Abstract:
Disclosed herein are systems and method for segmentation and identification of structured features in images. According to an aspect, a method may include representing an image as a graph of nodes connected together by edges. For example, the image may be an ocular image showing layered structures or other features of the retina. The method may also include adding, to the graph, nodes adjacent to nodes along first and second sides of the graph. The added nodes may have edge weights less than the nodes along the first and second sides of the graph. Further, the method may include assigning start and end points to any of the added nodes along the first and second sides, respectively. The method may also include graph cutting between the start and end points for identifying a feature in the image.
Abstract:
Systems and methods of optical coherence tomography stereoscopic imaging for microsurgery visualization are disclosed. In accordance with an aspect, a method includes capturing a plurality of cross-sectional images of a subject. The method includes generating a stereoscopic left image and right image of the subject based on the cross-sectional images. Further, the method includes displaying the stereoscopic left image and the right image in a display of a microscope system.
Abstract:
Systems and methods of optical coherence tomography stereoscopic imaging for microsurgery visualization are disclosed. In accordance with an aspect, a method includes capturing a plurality of cross-sectional images of a subject. The method includes generating a stereoscopic left image and right image of the subject based on the cross-sectional images. Further, the method includes displaying the stereoscopic left image and the right image in a display of a microscope system.
Abstract:
Disclosed herein are systems and method for segmentation and identification of structured features in images. According to an aspect, a method may include representing an image as a graph of nodes connected together by edges. For example, the image may be an ocular image showing layered structures or other features of the retina. The method may also include adding, to the graph, nodes adjacent to nodes along first and second sides of the graph. The added nodes may have edge weights less than the nodes along the first and second sides of the graph. Further, the method may include assigning start and end points to any of the added nodes along the first and second sides, respectively. The method may also include graph cutting between the start and end points for identifying a feature in the image.
Abstract:
Disclosed herein are systems and method for segmentation and identification of structured features in images. According to an aspect, a method may include representing an image as a graph of nodes connected together by edges. For example, the image may be an ocular image showing layered structures or other features of the retina. The method may also include adding, to the graph, nodes adjacent to nodes along first and second sides of the graph. The added nodes may have edge weights less than the nodes along the first and second sides of the graph. Further, the method may include assigning start and end points to any of the added nodes along the first and second sides, respectively. The method may also include graph cutting between the start and end points for identifying a feature in the image.