Local Area Signal-to-Noise Ratio (LASNR) algorithm for Image Segmentation

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Many automated image-based applications have need of finding small spots in a variably noisy image. For humans, it is relatively easy to distinguish objects from local surroundings no matter what else may be in the image. We attempt to capture this distinguishing capability computationally by calculating a measurement that estimates the strength of signal within an object versus the noise in its local neighborhood. First, we hypothesize various sizes for the object and corresponding background areas. Then, we compute the Local Area Signal to Noise Ratio (LASNR) at every pixel in the image, resulting in a new image with LASNR ... continued below

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Kegelmeyer, L; Fong, P; Glenn, S & Liebman, J July 3, 2007.

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Many automated image-based applications have need of finding small spots in a variably noisy image. For humans, it is relatively easy to distinguish objects from local surroundings no matter what else may be in the image. We attempt to capture this distinguishing capability computationally by calculating a measurement that estimates the strength of signal within an object versus the noise in its local neighborhood. First, we hypothesize various sizes for the object and corresponding background areas. Then, we compute the Local Area Signal to Noise Ratio (LASNR) at every pixel in the image, resulting in a new image with LASNR values for each pixel. All pixels exceeding a pre-selected LASNR value become seed pixels, or initiation points, and are grown to include the full area extent of the object. Since growing the seed is a separate operation from finding the seed, each object can be any size and shape. Thus, the overall process is a 2-stage segmentation method that first finds object seeds and then grows them to find the full extent of the object. This algorithm was designed, optimized and is in daily use for the accurate and rapid inspection of optics from a large laser system (National Ignition Facility (NIF), Lawrence Livermore National Laboratory, Livermore, CA), which includes images with background noise, ghost reflections, different illumination and other sources of variation.

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PDF-file: 11 pages; size: 0.1 Mbytes

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  • Presented at: SPIE Symposium on Optical Engineering & Applications - Applications of Digital Image Processing, San Diego, CA, United States, Aug 26 - Aug 30, 2007

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  • Report No.: UCRL-CONF-232852
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 920872
  • Archival Resource Key: ark:/67531/metadc893219

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

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  • July 3, 2007

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  • Sept. 27, 2016, 1:39 a.m.

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  • Nov. 29, 2016, 6:50 p.m.

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Kegelmeyer, L; Fong, P; Glenn, S & Liebman, J. Local Area Signal-to-Noise Ratio (LASNR) algorithm for Image Segmentation, article, July 3, 2007; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc893219/: accessed November 24, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.