Bayesian estimation of growth age using shape and texture descriptors

S. Mahmoodi, B. S. Sharif, E. G. Chester, J. P. Owen, R. E.J. Lee

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations

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.

Original languageBritish English
Pages (from-to)489-493
Number of pages5
JournalIEE Conference Publication
Issue number465 II
DOIs
StatePublished - 1999
EventProceedings of the 1999 7th International Conference on Image Processing and its Applications - Manchester, UK
Duration: 13 Jul 199915 Jul 1999

Fingerprint

Dive into the research topics of 'Bayesian estimation of growth age using shape and texture descriptors'. Together they form a unique fingerprint.

Cite this