Abstract:
Computer-implemented image processing methods and apparatuses are presented for automatically selecting regions of interest within an image represented by pixel intensity values. A first pixel box is employed in progressively scanning and evaluating the image. If pixels within the first pixel box have pixel-intensity-related characteristics exceeding respective defined thresholds, then those pixels are identified as an area of interest and a second pixel box is employed in progressively scanning and evaluating the selected area of interest to identify regions of interest. Each area of interest is larger than a region of interest, and the second pixel box is smaller than the first. Regions of interest within the image are identified if one or more pixel-intensity-related characteristics of pixels within the second pixel box exceeds a second defined threshold, wherein the second threshold is greater than the first. Once selected, identifying information for the regions of interest is stored or output.
Abstract:
Computer-implemented image processing methods and apparatuses are presented for automatically selecting regions of interest within an image represented by pixel intensity values. A first pixel box is employed in progressively scanning and evaluating the image. If pixels within the first pixel box have pixel-intensity-related characteristics exceeding respective defined thresholds, then those pixels are identified as an area of interest and a second pixel box is employed in progressively scanning and evaluating the selected area of interest to identify regions of interest. Each area of interest is larger than a region of interest, and the second pixel box is smaller than the first. Regions of interest within the image are identified if one or more pixel-intensity-related characteristics of pixels within the second pixel box exceeds a second defined threshold, wherein the second threshold is greater than the first. Once selected, identifying information for the regions of interest is stored or output.
Abstract:
Automated FRET imaging of membrane-bound receptor/ligand complexes can discriminate between a clustered organization of ligand/receptor complexes that occurs during the early endocytic stages following internalization and a random distribution characteristic of late stage disassociation of ligand from the receptor. In the case of the low density lipoprotein receptor (LDL-R) and its ligand, LDL, this feature of FRET imaging forms the basis of an assay to monitor the endosomal release of cholesterol into the cell and identify compounds which alter pH in the endosome thereby inhibiting the disassociation of ligand and cholesterol from the receptor, a mechanism that is involved in regulation of plasma/serum cholesterol.
Abstract:
Automated FRET imaging of membrane-bound receptor/ligand complexes can discriminate between a clustered organization of ligand/receptor complexes that occurs during the early endocytic stages following internalization and a random distribution characteristic of late stage disassociation of ligand from the receptor. In the case of the low density lipoprotein receptor (LDL-R) and its ligand, LDL, this feature of FRET imaging forms the basis of an assay to monitor the endosomal release of cholesterol into the cell and identify compounds which alter pH in the endosome thereby inhibiting the disassociation of ligand and cholesterol from the receptor, a mechanism that is involved in regulation of plasma/serum cholesterol.