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Bayesian estimation of growth age using shape and texture descriptors

Lookup NU author(s): Dr Sasan Mahmoodi, Professor Bayan Sharif, Dr Graeme Chester, Dr John Owen, Dr Richard Lee


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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.

Publication metadata

Author(s): Owen JP; Sharif BS; Mahmoodi S; Chester EG; Lee REJ

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Seventh International Conference on Image Processing and Its Applications

Year of Conference: 1999

Pages: 489-493

Publisher: IEEE


DOI: 10.1049/cp:19990370

Notes: TY - JOUR U1 - 99114912851 Compilation and indexing terms, Copyright 2004 Elsevier Engineering Information, Inc. U2 - Bayesian methods Active shape models

Library holdings: Search Newcastle University Library for this item

ISBN: 9780852967171