Morphometric analysis for pathological abnormality detection in the skull vaults of adolescent idiopathic scoliosis girls.
In this paper, we present a comprehensive framework to detect morphological changes in skull vaults of adolescent idiopathic scoliosis girls. To our knowledge, this is the first attempt to use a combination of medical knowledge, image analysis techniques, statistical learning tools, and scientific visualization methods to detect skull morphological changes. The shape analysis starts from a reliable 3-D segmentation of the skull using thresholding and math-morphological operations. The gradient vector flow is used to model the skull vault surface, which is followed by a spherically uniform sampling. The scale-normalized distances from the shape centroid to sample points are defined as the features. The most discriminative features are selected using recursive feature elimination for support vector machine. The results of this study specify the skull vault surface changes and shed light on building the evidence of bone formation abnormality in AIS girls.
Shi
Lin
L
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China. lshi@cse.cuhk.edu.hk
Heng
Pheng Ann
PA
Wong
Tien-Tsin
TT
Chu
Winnie C W
WC
Yeung
Benson H Y
BH
Cheng
Jack C Y
JC
D000293:Adolescent; D000465:Algorithms; D001185:Artificial Intelligence; D002648:Child; D003198:Computer Simulation; D019465:Craniofacial Abnormalities / Q000473:pathology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012600:Scoliosis / Q000473:pathology*; D012680:Sensitivity and Specificity; D012886:Skull / Q000002:abnormalities* / Q000473:pathology*