Image segmentation using joint spatial-intensity-shape features: Application to CT lung nodule segmentation

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Volumn 7259, Issue , 2009, Pages

  Ye, Xujiong ^a   Siddique, Musib ^a   Douiri, Abdel ^a   Beddoe, Gareth ^a   Slabaugh, Greg ^a  

^a Medicsight PLC   (United Kingdom)

Author keywords

Mean shift; Modified expectation maximization (MEM); Pulmonary nodules; Segmentation; Shape index

Indexed keywords

MEAN SHIFT; MODIFIED EXPECTATION-MAXIMIZATION; MODIFIED EXPECTATION-MAXIMIZATION (MEM); PULMONARY NODULES; SHAPE INDICES;

CLUSTERING ALGORITHMS; COMPUTERIZED TOMOGRAPHY; IMAGE ACQUISITION; IMAGE SEGMENTATION; IMAGING SYSTEMS; MAXIMUM PRINCIPLE; MEDICAL IMAGING; OPTIMIZATION; TARGET TRACKING; VECTOR QUANTIZATION; VISUAL COMMUNICATION;

THREE DIMENSIONAL;

EID: 71649088628     PISSN: 16057422     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.811151     Document Type: Conference Paper

References (13)

1. I.Sluimer, A.Schiham, M.Prokop, and B.V.Ginneken, "Computer analysis of computed tomography scans of the lung: A survey," IEEE Trans. Medical Imaging, vol. 25, no.4, pp.385-405, 2006.
2. N.Xu, N.Ahuja, and R.Bansal, "Automated lung nodule segmentation using dynamic programming and EM-based classification", Proc. SPIE Med. Image, 2002, vol.4684, pp.666-676.
3. Y.Kawata, N.Niki, H.Ohmatsu, R.Kakinuma, K.Eguchi, M.Kaneko, and N.Moriyama, "Quantitative surface characterization of pulmonary nodules based on thin-section CT images", IEEE Trans Nucl Sci 1998: 45:2132-2138.
4. Y.Kawata, N.Noboru, H.Ohmatsu, N.Moriyama, "A deformable surface model based on boundary and region information for pulmonary nodule segmentation from 3D thoracic CT images", IEICE Trans. Information and Systems, 2003; E86-D:1921-1930.
5. W.Mullally, M.Betke, J.Wang, and J.P.Ko, "Segmentation of nodules on chest computed tomography for growth assessment", Medical Physics, April 2004, 31(4), pp.839-848.
6. L.Fan, J.Qian, B.L.Odry, and H.Shen, "Automatic segmentation of pulmonary nodules by using dynamic 3D cross-correlation for interactive CAD systems", Proc. SPIE Med. Image, 2002, vol.4684, pp.1362-1369.
7. K.Okada, D. Comaniciu, A. Krishnan, "Robust anisotropic gaussian fitting for volumetric characterization of pulmonary nodules in multislice CT", IEEE Trans. Medical Imaging, 2005, vol.24, no.3, 409-423.
8. J.Dehmeshki, H.Amin, and X.Ye, "Segmentation of Pulmonary Nodules in Thoracic CT scans: A Region Growing Approach," IEEE Trans. Medical Imaging, vol.27, no.4, 2008.
9. K.Fukunaga and L.D.Hostetler, "Estimation of the gradient of a density function with applications in pattern recognition," vol.IT-21, pp.32-40, 1975.
10. D.Comaniciu and P.Meer, "Mean shift: A robust approach toward feature space analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, pp.603-619, 2002.
11. O.Monga and S. Benayoun, "Using partial derivatives of 3D images to extract typical surface features," Computer Vision and Image Understanding, vol.61, pp.171-189, 1995.
12. H.Yoshida and J. Nappi, "Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps," IEEE Trans. Medical Imaging, vol.20, no.12, pp.1261-1273, 2001.
13. S. Z. Li, "Markov Random Field Modeling in Computer Vision," Springer-Verlag, 1995.