Image analysis techniques for characterizing disc space narrowing in cervical vertebrae interfaces.
Image analysis techniques are introduced for evaluating disc space narrowing of cervical vertebrae interfaces from X-ray images. Four scale-invariant, distance transform-based features are presented for characterizing the spacing between adjacent vertebrae. K-means and self-organizing map clustering techniques are applied to estimate the degree of disc space narrowing using a four grade (0-3) scoring system, where 0 and 3 represent normal spacing and significant narrowing, respectively. For a data set of 294 vertebrae interfaces, experimental results yield average correct grade assignment of greater than 82.10% for each of the four grades using a one grade window around the correct grade.
Chamarthy
Pavan
P
Department of Electrical and Computer Engineering, University of Missouri-Rolla, Rolla, MO 65409, USA.
Stanley
R Joe
RJ
Cizek
Gregory
G
Long
Rodney
R
Antani
Sameer
S
Thoma
George
G
D000465:Algorithms; D002574:Cervical Vertebrae / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007403:Intervertebral Disc / Q000000981:diagnostic imaging* / Q000473:pathology; D011859:Radiography; D013122:Spinal Diseases / Q000000981:diagnostic imaging