Conference Paper

Adaptive Detection of Hotspots in Thoracic Spine from Bone Scintigraphy.

DOI: 10.1007/978-3-642-24955-6_31 Conference: Neural Information Processing - 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part I
Source: DBLP

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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    ABSTRACT: We propose a novel method for the segmentation of spine and ribs from posterior whole-body bone scintigraphy images. The knowledge-based method is first applied to determine the thoracic region. An adaptive thresholding method is then used to extract the thoracic spine from it. The rib segmentation algorithm is carried out in two steps. First, the rib skeleton is extracted based on standard template and image information. The skeleton is then used to locate the accurate boundary of the respective ribs. The introduction of standard template can deal with significant variations among different patients well, while the skeleton-based method is robust against the low contrast between the ribs and the adjacent intervals. The experiments show that our method is robust and accurate compared to existing methods.
    Neural Information Processing - 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part I; 01/2011
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