Toggle Main Menu Toggle Search

Open Access padlockePrints

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

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


Abstract

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

URL: http://dx.doi.org/10.1049/cp:19990370

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


Share