Title :
Neural Network Based Spinal Age Estimation Using Lumbar Spine Magnetic Resonance Images (MRI)
Author :
Khan, Ajmal ; Iliescu, Dragos ; Hines, E. ; Hutchinson, C. ; Sneath, R.
Author_Institution :
Sch. of Eng., Univ. of Warwick, Coventry, UK
Abstract :
A human spine is a complicated structure of bones, joints, ligaments and muscles which all undergo a process of change with the age. This paper describes the existence of a pattern in degenerative process of human spine. Unveiling this pattern will be helpful in reassuring patients that the results of their scan are not unusual or indicative of any disease. A model based on artificial neural networks was formed with the help different spinal features such as vertebral height, disc height, disc signal and para-spinal muscles etc. These features of the lumbar spine vary with the age. Proposed model puts the degenerative changes of the lumbar spine into the context of a normal ageing process to estimate patient "spinal age". This research work will provide a more concrete view of spinal growth in human being with the help of statistical features. This work will be helpful in drawing a borderline between normal, under and over growth of the human spine with respect to the person\´s age.
Keywords :
biomedical MRI; bone; medical image processing; muscle; neural nets; artificial neural networks; bones; disc height; disc signal; human spine; joints; ligaments; lumbar spine magnetic resonance images; muscles; neural network based spinal age estimation; paraspinal muscles; patient age estimation; spinal features; statistical features; vertebral height; Back; Magnetic resonance imaging; Neural networks; Neurons; Pain; Spine; Training; lumbar spine; degenerative changes; neural network; spinal age; magnetic resonance imaging;
Conference_Titel :
Intelligent Systems Modelling & Simulation (ISMS), 2013 4th International Conference on
Conference_Location :
Bangkok
Print_ISBN :
978-1-4673-5653-4
DOI :
10.1109/ISMS.2013.101