Abstract
In this paper, we propose an adaptive algorithm for the detection of hotspots in thoracic spine from bone scintigraphy. The intensity distribution of spine is firstly analyzed. The Gaussian fitting curve for the intensity distribution of thoracic spine is estimated, in which the influence of hotspots is eliminated. The accurate boundary of hotspot is delineated via adaptive region growing algorithm. Finally, a new deviation operator is proposed to train the Bayes classifier. The experiment results show that the algorithm achieve high sensitivity (97.04%) with 1.119 false detections per image for hotspot detection in thoracic spine.
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Chang, Q., Wang, Q., Qiao, Y., Zhu, Y., Huang, G., Yang, J. (2011). Adaptive Detection of Hotspots in Thoracic Spine from Bone Scintigraphy. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_31
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DOI: https://doi.org/10.1007/978-3-642-24955-6_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24954-9
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