Graph image language techniques supporting radiological, hand image interpretations
Computer Vision and Image Understanding
Hand radiograph image segmentation using a coarse-to-fine strategy
Pattern Recognition
An object detection and recognition system for weld bead extraction from digital radiographs
Computer Vision and Image Understanding
Skeletal growth estimation using radiographic image processing and analysis
IEEE Transactions on Information Technology in Biomedicine
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Automatic segmentation of X-ray hand images is an important process. In studies such as skeletal bone age assessment, bone densitometry and analyzing of bone fractures, it is a necessary extremely difficult and complicated task. In this study, hand X-ray images were segmented by using C-means classifier. Extraction of bone tissue was realized in three steps: i) preprocessing, ii) feature extraction and iii) automatic segmentation. In preprocessing scheme, inhomogeneous intensity distribution is eliminated and some structural pre-information about hand was obtained in order to use in feature extraction block. In feature extraction process, edges between soft and bone tissues were extracted by proposed enhancement process. In automatic segmentation process, the image was segmented using C-mean classifier by taking care of local information. In the study, hand images of ten different people were segmented with high performances above 95%.