Detection of osteoporosis by morphological granulometries

Edward R. Dougherty, Yidong Chen, Saara M.D. Totterman, Joseph P. Hornak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Local morphological granulometries are generated by opening an image successively by an increasing family of structuring elements and, at each pixel, keeping an image area count in a fixed-size window about the pixel. After normalization there is at each pixel a probability density, called a `local pattern spectrum,' and the moments of this density are used to classify the pixel according to surrounding texture. The method having been developed for binary images, the present paper applies a gray-scale version of the methodology to detect osteoporosis in magnetic resonance (MR) images of the wrist. Maximum-likelihood classification is used to apply the local-pattern-spectra moment information. Owing to the presence of a continuous intertwined network of bone fibers called trabeculae, when imaged by an MR imaging system a normal region of bone tissue possesses a coarse, grainy texture resulting in characteristic granulometric features. Osteoporosis is a metabolic bone disease typified by a gradual loss of trabecular bone, and this loss is revealed by significant changes in the granulometric features, thereby leading to detection.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherPubl by Int Soc for Optical Engineering
Number of pages15
Editionpt 1
ISBN (Print)081940814X
StatePublished - 1992
Externally publishedYes
EventBiomedical Image Processing and Three-Dimensional Microscopy. Part 1 (of 2) - San Jose, CA, USA
Duration: Feb 10 1991Feb 13 1991

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Numberpt 1
ISSN (Print)0277-786X


OtherBiomedical Image Processing and Three-Dimensional Microscopy. Part 1 (of 2)
CitySan Jose, CA, USA

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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