DocumentCode :
1949201
Title :
A Novel Automatic Framework for Scoliosis X-Ray Image Retrieval
Author :
XU, Zhiping ; Pan, Jinhong ; Zhang, Shiyong
Author_Institution :
Fudan Univ., Shanghai
fYear :
2007
fDate :
12-17 Aug. 2007
Firstpage :
2482
Lastpage :
2485
Abstract :
The paper proposed a novel automatic scoliosis X-ray image retrieval framework based on the global statistical feature of edge, edge co-occurrence matrix (ECM) and the local geometrical feature set of the whole spine, angle of each spine curve. The ECM is based on the statistical feature attained from the edge detection operators which applied on the image. The eigenvectors obtained from principle component analysis (PCA) of the ECM can preserve the high spatial frequencies components, so they are well suited for shape as well as texture representation. The geometrical feature like the Cobb´s angle of each spine curve could be derived from the image segmentation based on the Intersecting Cortical Model, which is elicitation of the Eckhorn´s model. The experiment shows that the framework shows good accuracy for the input query X-ray image in our work.
Keywords :
X-ray imaging; edge detection; eigenvalues and eigenfunctions; feature extraction; image representation; image retrieval; image segmentation; image texture; matrix algebra; medical image processing; principal component analysis; Intersecting Cortical Model; edge cooccurrence matrix; edge detection; eigenvectors; global statistical feature; image segmentation; local geometrical feature set; principle component analysis; scoliosis; spine curve; texture representation; x-ray image retrieval; Electrochemical machining; Frequency; Image edge detection; Image retrieval; Image segmentation; Image texture analysis; Principal component analysis; Shape; Solid modeling; X-ray imaging;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
ISSN :
1098-7576
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2007.4371348
Filename :
4371348
Link To Document :
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