Abstract
A robust, efficient segmentation algorithm for automatic segmentation of MR images of the metacarpophalangeal joint is presented. A preliminary segmentation detects bones in MR scans and uses histogram analysis, morphological operations and knowledge based rules to classify various tissues in the joint. The second part of the algorithm improves the segmentation mask and refines boundaries of bones using minimization of a sum of square deviations, automatic signal segmentation into an optimum number of segments, graph theory, and statistical analysis. The algorithm has been tested on 9 MR patient studies and detects 97% of all existing bones correctly with an average exceeding 80% mutual overlap between ground truth and detected regions
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© 2005 Springer-Verlag Berlin Heidelberg
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Kubassova, O., Boyle, R.D., Pyatnizkiy, M. (2005). Bone Segmentation in Metacarpophalangeal MR Data. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_80
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DOI: https://doi.org/10.1007/11552499_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
Online ISBN: 978-3-540-31999-3
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