Bayesian estimation of growth age using shape and texture descriptors

  1. Lookup NU author(s)
  2. Dr Sasan Mahmoodi
  3. Professor Bayan Sharif
  4. Dr Graeme Chester
  5. Dr John Owen
  6. Dr Richard Lee
Author(s)Owen JP; Sharif BS; Mahmoodi S; Chester EG; Lee REJ
Editor(s)
Publication type Conference Proceedings (inc. Abstract)
Conference NameSeventh International Conference on Image Processing and Its Applications
Conference LocationManchester, UK
Year of Conference1999
Date13-15 July 1999
Volume465 (2)
Pages489-493
0537-9989
ISBN9780852967171
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This paper presents an automated growth estimation system based on Bayesian principle by using knowledge-based vision methods to localize and segment bones in hand radiographs. Traditional manual methods have been tedious and prone to inter and intra observer inconsistencies. A robust segmentation algorithm known as Active Shape Models (ASM) followed by a hierarchical bone localization scheme is used to detect bone contours and also to produce a shape descriptor of bone development. Traditional image processing techniques are applied to generate different descriptors for bone shapes. A Bayesian decision-making algorithm is then applied to the descriptors for growth estimation purposes. The estimation accuracy was 85% for females and 83% for males, which suggests that the proposed approach has a potential application in paediatric medicine.
PublisherIEEE
URLhttp://dx.doi.org/10.1049/cp:19990370
DOI10.1049/cp:19990370
NotesTY - JOUR U1 - 99114912851 Compilation and indexing terms, Copyright 2004 Elsevier Engineering Information, Inc. U2 - Bayesian methods Active shape models
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