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S. Ukil and J. M. Reinhardt. Smoothing Lung Segmentation Surfaces in 3D X-ray CT Images using Anatomic Guidance. Acad. Radiol., vol. 12, no. 12, pp. 1502-1511, 2005.

Abstract: Rationale and Objectives: Automatic lung segmentation in volumetric computed tomography (CT) images has been extensively investigated, and several methods have been proposed. Most methods distinguish the lung parenchyma from the surrounding anatomy based on the difference in CT attenuation values. This leads to an irregular and inconsistent lung boundary for the regions near the mediastinum, which can cause inconsistent boundaries both across subjects, and within subjects scanned at different intervals of time. Processes like lung image registration and lung atlas construction can be affected by such inconsistencies. Therefore there is a need for a more consistent lung surface near the mediastinum. Materials and Methods: This paper presents a fully automatic method for the 3D smoothing of the lung boundary using information from the segmented human airway tree. First, using the segmented airway tree we define a bounding box around the mediastinum for each lung, within which all operations are performed. We then define all generations of the airway tree distal to the right and left mainstem bronchi to be part of the respective lungs, and exclude all other segments. Finally, we perform a fast morphological closing with an ellipsoidal kernel to smooth the surface of the lung. Results: This method has been tested by processing the segmented lungs from eight normal datasets. The mean value of the magnitude of curvedness of the lung contours, averaged over all the datasets, is 0.13 post smoothing, compared to 4.91 before smoothing. The accuracy of the lung contours after smoothing is assessed by comparing the automatic results to manually traced smooth lung borders by a human analyst. Averaged over all volumes, the root mean square difference between human and computer borders is 0.8691 mm after smoothing, compared to 1.3012 mm before smoothing. The mean similarity index, which is an area overlap measure based on the kappa statistic, is 0.9958 (SD 0.0032), after smoothing. Conclusions: We have described a novel scheme for smoothing the lung contour around the mediastinum. The method is based on using anatomic information from the segmented airway tree. The validation results show that there is good agreement between manual and computer results. Since there is no accepted criteria for defining the lung boundary near the mediastinum, we believe our method of defining the boundary based on the structure of the airway tree provides a good basis for 3D smoothing.

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Keywords: lung segmentation shape

Other publications by: S. Ukil, J. M. Reinhardt

Related journal papers:

J. M. Reinhardt and W. E. Higgins. Paradigm for Shape-Based Image Analysis. Optical Engineering, vol. 37, no. 2, pp. 570-581, 1998. (details)
J. M. Reinhardt, A. J. Wang, T. P. Weldon, and W. E. Higgins. Cue-Based Segmentation of 4D Cardiac Image Sequences. Comp. Vision and Image Understanding, vol. 77, no. 2, pp. 251-262, 2000. (details)
S. Hu, E. A. Hoffman, and J. M. Reinhardt. Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Trans. Medical Imaging, vol. 20, no. 6, pp. 490-498, 2001. (details)
J. M. Reinhardt and W. E. Higgins. Comparison between the Morphological Skeleton and Morphological Shape Decomposition. IEEE Trans. Patt. Anal. Machine Intell., vol. 18, no. 9, pp. 951-957, 1996. (details)
J. M. Reinhardt and W. E. Higgins. Efficient Morphological Shape Representation. IEEE Trans. Image Proc., vol. 5, no. 1, pp. 89-101, 1996. (details)

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B. Li and J. M. Reinhardt. Automatic generation of 3-D shape models and their application to tomographic image segmentation. In M. Sonka and K. M. Hansen, eds., Proc. SPIE Conf. Medical Imaging, vol. 4322, pp. 311-322, San Diego, CA, 2001. (details)
S. Ukil and J. M. Reinhardt. Smoothing Lung Segmentation Surfaces in 3D X-ray CT Images using Anatomic Guidance. In Proc. SPIE Conf. Medical Imaging, vol. 5370, pp. 1066-1075, 2004. (details)
W. E. Higgins, J. M. Reinhardt, and W. L. Sharp. Semi-automatic construction of 3D medical image-segmentation processes. In R. A. Robb, ed., Proc. SPIE Conf. Visual. in Biomed. Comp., vol. 2359, pp. 59-71, Rochester, MN, 1994. (details)
J. M. Reinhardt and W. E. Higgins. Automatic Generation of Image-Segmentation Processes. In Proc. IEEE Int. Conf. Image Processing, vol. III, pp. 791-795, 1994. (details)
J. M. Reinhardt and W. E. Higgins. Strategy for shape-based image analysis. In Proc. IEEE Int. Conf. Image Processing, vol. I, pp. 502-505, 1994. (details)

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D. Aykac. Segmentation and analysis of the human airway tree from 3D X-ray CT images. MS thesis, The University of Iowa, Iowa City, IA, 2000. (details)
K. Ding. Registration-based regional lung mechanical analysis. MS thesis, The University of Iowa, Iowa City, IA, 2008. (details)
B. Li. The construction of a normative human lung atlas by inter-subject registration and warping of CT images. PhD thesis, The University of Iowa, Iowa City, IA, 2004. (details)
Y. Pan. Estimation of regional lung expansion via 3D image registration. MS thesis, The University of Iowa, Iowa City, IA, 2005. (details)
S. Ukil. Anatomy-guided lung lobe segmentation and fissure analysis in X-ray CT images. PhD thesis, The University of Iowa, Iowa City, IA, 2006. (details)