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Bayesian estimation of growth age using shape and texture descriptors

Lookup NU author(s): Dr Graeme Chester, Professor Bayan Sharif


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This paper presents an automated growth estimation system based on Bayesian principle by using knowledge-based vision methods to localise and segment bones in hand radiographs. Traditional manual methods have been tedious and prone to inter and intra observer inconsistencies. A segmentation algorithm known as active models (ASM) followed by a hierarchical bone localisation 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): Chester EG, Mahmoodi S, Sharif BS, Owen JP

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Seventh International Congress on Image Processing and its Applications

Year of Conference: 1999

Pages: 489-493

ISSN: 0852967179

Publisher: IEEE


DOI: 10.1049/cp:19990370