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
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.
- [Show abstract] [Hide abstract]
ABSTRACT: Bone scintigraphy is a useful tool in diagnosing bone diseases and accurate segmentation of ribs in bones can data-sets is an essential step for accurate diagnosis. It's a challenging task to demonstrate accurate results due to poor image quality and much extra priority should be introduced for performance improvement. To complete this task, we propose a new Markov random Field (MRF) based framework for automatic segmentation in this paper. In particularly, a series of constraints that force the coarse result of Segmentation to have smooth boundary and connected region are defined. In MRF model, segmentation problem is formed as a discrete labeling problem, so how to unify various constraints into the first-order and secondorderclique potentials of energy function is another challenge which is also studied in the paper. Our main contribution can be summarized as, 1 A framework (GSCSP-B) which is the first detailed method using MRF for segmenting ribs in Bone-Scan image is proposed.2 As to the implementation issue, a method for generating seeds in graph-cut automatically is developed for better user experience.3 Propose a series of constraints, especially the geodesic distance and boundary smoothness constraints and the method for incorporating them into MRF model is presented as well.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.