Size-invariant descriptors for detecting regions of abnormal growth in cervical vertebrae.
Digitized spinal X-ray images exhibiting specific pathological conditions such as osteophytes can be retrieved from large databases using Content Based Image Retrieval (CBIR) techniques. For efficient image retrieval, it is important that the pathological features of interest be detected with high accuracy. In this study, new size-invariant features were investigated for the detection of anterior osteophytes, including claw and traction in cervical vertebrae. Using a K-means clustering and nearest neighbor classification approach, average correct classification rates of 85.80%, 86.04% and 84.44% were obtained for claw, traction and anterior osteophytes, respectively.
Stanley
R Joe
RJ
Department of Electrical and Computer Engineering, University of Missouri-Rolla, Rolla, MO, United States. stanleyr@umr.edu
Antani
Sameer
S
Long
Rodney
R
Thoma
George
G
Gupta
Kapil
K
Das
Mohammed
M
D002574:Cervical Vertebrae / Q000000981:diagnostic imaging* / Q000473:pathology*; D016000:Cluster Analysis; D003937:Diagnosis, Differential; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D054850:Osteophyte / Q000145:classification / Q000000981:diagnostic imaging / Q000473:pathology; D010363:Pattern Recognition, Automated / Q000379:methods; D011237:Predictive Value of Tests; D011859:Radiography; D011873:Radiology Information Systems / Q000592:standards; D012680:Sensitivity and Specificity; D013128:Spinal Osteophytosis / Q000145:classification / Q000000981:diagnostic imaging* / Q000473:pathology