University of Florida Research Foundation Inc

Sentinel Diagnostic Imaging, Inc.

Daniel John Dickrell, Iii

David L Meadows

Richard D Clark, Iii

Wallace Gregory Sawyer

Jacob J Panikulam

Disclosed herein is a method comprising imaging a network section through which flow occurs; where the flow is selected from a group consisting of fluid, electrons, protons, neutrons and holes; partitioning the image into sub-regions based on metabolic need and function; where each region comprises one or more sources and one or more sinks; where the flow emanates from the source and exits into the sinks; performing a Delaunay triangulation tessellation on one or more sub-regions by connecting one or more sources and one or more sinks; where the Delaunay triangulations maximize the minimum angle of all the angles of the triangles in the triangulation; generating a Voronoi diagram from the Delaunay triangulation by subdividing the sub-regions into Voronoi cells, where each Voronoi cell contains exactly one sink or one source; and where the intersections of Voronoi cells are Voronoi cell vertices; locating a sink endpoint centroid; connecting a source to a nearest Voronoi cell vertex; connecting at least one sink to at least one of the remaining Voronoi cell vortices to complete the network; and performing a smoothing function on the network to form a smoothed network.

System and method for diagnosing defects in random flow systems

Disclosed herein is a method comprising a method comprising imaging a network section through which flow occurs; where the flow is selected from a group consisting of fluid, electrons, protons, neutrons and holes; partitioning the image into sub-regions based on metabolic need and function; where each region comprises one or more sources and one or more sinks; where the flow emanates from the source and exits into the sinks; generating a Voronoi diagram from the Delaunay triangulation by subdividing the sub-regions into Voronoi cells, where each Voronoi cell contains exactly one sink or one source; and where the intersections of Voronoi cells are Voronoi cell vertices; calculating a flow rate in each Voronoi cell; and according a color to Voronoi cells based on their flow rates; where Voronoi cells having similar rates are accorded similar colors.

System for diagnosing disease using spatial area normalization analysis

Disclosed herein is a method that comprises obtaining an image of a network section through which flow occurs; where the flow is selected from a group consisting of fluid, electrons, protons, neutrons and holes; subjecting the image to a low pass filter to increase contrast in portions of the network sections; computing a local mean of visible light intensity at each pixel that is present in the image; calculating a visible light intensity difference between each pixel and the local mean of visible light intensity and producing a differentiated image using this calculation; creating a base image of the differentiated image; where the base image comprises a hand segmented gold standard dataset; removing objects below a minimum threshold size from the base image; and retaining the remaining objects if they approximate the line or spine.

Method for the autonomous image segmentation of flow systems

Disclosed herein is a system and a method for analyzing apparent random pathways, patterns, networks, or a series of events and characterizing these apparent random pathways, patterns, networks, or a series of events by constructal analysis. The resulting statistical values obtained can be used to compare the apparent random pathways, patterns, networks, or a series of events with other apparent random pathways, patterns, networks, or a series of events. The comparison can yield knowledge about the apparent random pathways, patterns, networks, or a series of events as well as the neighborhood or surroundings of the apparent random pathways, patterns, networks, or a series of events.

Growing community of inventors

Gainesville, FL, United States of America

Daniel John Dickrell, Iii