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|標題:||EXCLUDING BACKGROUND INITIAL SEGMENTATION FOR RADIOGRAPHIC IMAGE SEGMENTATION||作者:||Shu, S.G.
|關鍵字:||Bone age;Feature extraction;Segmentation;Bone age assessment;2-means;GVF snake;bone-age assessment||Project:||International Journal of Innovative Computing Information and Control||期刊/報告no：:||International Journal of Innovative Computing Information and Control, Volume 5, Issue 11A, Page(s) 3849-3860.||摘要:||
Bony tissue extraction of phalangeal ROI of radiographic images highly affects the results of feature extraction and bone age assessment. The performance of most segmentation techniques, such as k-means, snakes, and so oil, relies on the precision of a given initial segmentation. Most papers provide the initial segmentation by random or manual choices. Here, an excluding background initial segmentation method is proposed to overcome the initial segmentation problem for feature extraction oil phalangeal ROI. Sobel, 2-means, Canny edge-detection and watershed methods are used to provide an initial segmentation and compared to the proposed method by applying adaptive 2-means and GVF snake to do the finial segmentation. The experiment results show that the proposed excluding background initial segmentation method together with adaptive 2-means clustering method provides a very well automatic segmentation ability to separate accurately the epiphysis and metaphysis from the soft tissue of hand radiographs at the early stage of skeletal development.
|Appears in Collections:||資訊科學與工程學系所|
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