Abstract
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%.
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References
Felisberto, M.K., Lopes, H.S., Centeno, T.M., Arruda, L.V.R.: Object detection and recognition system for weld bead extraction from digital radiographs. Computer Vision and Image Understanding 102, 238–249 (2006)
Ogiela, M.R., Tadeusiewicz, R., Ogiela, L.: Graph image language techniques supporting radiological, hand image interpretations. Computer Vision and Image Understanding 103, 112–120 (2006)
Garcia, R.L., Fernandez, M.M., Arribas, J.I., Lopez, C.A.: A fully automatic algorithm for contour detection of bones in hand radiographs using active contours. In: International Conference on Image Processing, pp. 421–424 (2003)
Han, C.C., Lee, C.H., Peng, W.L.: Hand radiograph image segmentation using a coarse-to-fine strategy. Pattern Recognition 40, 2994–3004 (2007)
Zhang, A., Gertych, A., Liu, B.J.: Automatic bone age assessment for young children from newborn to 7-year-old using carpal bones. Computerized Medical Imaging and Graphics 31, 299–310 (2007)
Mahmoodi, S., Sharif, B.S., Chester, E.G., Owen, J.P., Lee, R.: Skeletal growth estimation using radiographic image processing and analysis. IEEE Transactions on Information Technology in Biomedicine 4, 292–297 (2000)
Pietka, E., Kurkowska, S.P., Gertych, A., Cao, F.: Integration of computer assisted bone age assessment with clinical PACS. Computerized Medical Imaging and Graphics 27, 217–228 (2003)
Sotoca, J.M., Inesta, J.M., Belmonte, M.A.: Hand bone segmentation in radioabsorptiometry images for computerised bone mass assessment. Computerized Medical Imaging and Graphics 27, 459–467 (2003)
Haidekker, M.A., Stevens, H.Y., Frangos, J.A.: Computerised methods for X-ray-based small bone densitometry. Computer Methods and Programs in Biomedicine 73, 35–42 (2004)
Jiang, Y., Babyn, P.: X-ray bone fracture segmentation by incorporating global shape model priors into geodesic active contours. In: Computer Assisted Radiology and Surgery, pp. 219–224 (2004)
Sharif, B.S., Chester, E.G., Owen, J.P., Lee, E.J.: Bone edge detection in hand radiographic images. In: Engineering Advances: New Opportunities for Biomedical Engineer, pp. 514–515 (1994)
Kurnaz, M.N.: Artımsal Yapay Sinir Ağları Kullanılarak Ultrasonik Görüntülerin Bölütlenmesi (in Turkish). Ph.D Thesis, İstanbul Technical University (2006)
Bocchi, L., Ferrara, F., Nicoletti, I., Valli, G.: An artificial neural network architecture for skeletal age assessment. In: International Conference on Image Processing, vol. 1, pp. 1077–1080 (2003)
Behiels, G., Maes, F., Vandermeulen, D., Suetens, P.: Evaluation of image features and search strategies for segmentation of bone structures in radiographs using Active Shape Models. Medical Image Analysis 6, 47–62 (2002)
Yuksel, A., Dokur, Z., Korurek, M., Olmez, T.: Modeling of inhomogeneous intensity distribution of X-ray source in radiographic images. In: 23rd International Symposium on Computer and Information Sciences, 2008. ISCIS 2008, pp. 1–5 (2008)
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Yuksel, A., Olmez, T. (2009). Automatic Segmentation of Bone Tissue in X-Ray Hand Images. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2009. Lecture Notes in Computer Science, vol 5495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04921-7_60
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DOI: https://doi.org/10.1007/978-3-642-04921-7_60
Publisher Name: Springer, Berlin, Heidelberg
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