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Proceedings Paper

3D segmentation and visualization of lung volume using CT
Author(s): Haibo Zhang; Xuejun Sun; Huichuan Duan
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Paper Abstract

Three-dimensional (3D)-based detection and diagnosis plays important role in significantly improving detection and diagnosis of lung cancers through computed tomography (CT). This paper presents a 3D approach for segmenting and visualizing lung volume by using CT images. An edge-preserving filter (3D sigma filter) is first performed on CT slices to enhance the signal-to-noise ratio, and wavelet transform (WT)-based interpolation incorporated with volume rendering is utilized to construct 3D volume data. Then an adaptive 3D region-growing algorithm is designed to segment lung mask incorporated with automatic seed locating algorithm through fuzzy logic algorithm, in which 3D morphological closing algorithm is performed on the mask to fill out cavities. Finally, a 3D visualization tool is designed to view the volume data, its projections or intersections at any angle. This approach was tested on single detector CT images and the experiment results demonstrate that it is effective and robust. This study lays groundwork for 3D-based computerized detection and diagnosis of lung cancer with CT imaging. In addition, this approach can be integrated into PACS system serving as a visualization tool for radiologists’ reading and interpretation.

Paper Details

Date Published: 12 April 2005
PDF: 9 pages
Proc. SPIE 5744, Medical Imaging 2005: Visualization, Image-Guided Procedures, and Display, (12 April 2005); doi: 10.1117/12.592996
Show Author Affiliations
Haibo Zhang, Shandong Normal Univ. (China)
Xuejun Sun, H. Lee Moffitt Cancer Ctr. and Research Institute (United States)
Huichuan Duan, Shandong Normal Univ. (China)

Published in SPIE Proceedings Vol. 5744:
Medical Imaging 2005: Visualization, Image-Guided Procedures, and Display
Robert L. Galloway; Kevin R. Cleary, Editor(s)

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