DocumentCode :
1244572
Title :
Robust anisotropic Gaussian fitting for volumetric characterization of Pulmonary nodules in multislice CT
Author :
Okada, Kazunori ; Comaniciu, Dorin ; Krishnan, Arun
Author_Institution :
Integrated Data Syst. Dept., Siemens Corp. Res., Princeton, NJ, USA
Volume :
24
Issue :
3
fYear :
2005
fDate :
3/1/2005 12:00:00 AM
Firstpage :
409
Lastpage :
423
Abstract :
This paper proposes a robust statistical estimation and verification framework for characterizing the ellipsoidal (anisotropic) geometrical structure of pulmonary nodules in the Multislice X-ray computed tomography (CT) images. Given a marker indicating a rough location of a target, the proposed solution estimates the target´s center location, ellipsoidal boundary approximation, volume, maximum/average diameters, and isotropy by robustly and efficiently fitting an anisotropic Gaussian intensity model. We propose a novel multiscale joint segmentation and model fitting solution which extends the robust mean shift-based analysis to the linear scale-space theory. The design is motivated for enhancing the robustness against margin-truncation induced by neighboring structures, data with large deviations from the chosen model, and marker location variability. A chi-square-based statistical verification and analytical volumetric measurement solutions are also proposed to complement this estimation framework. Experiments with synthetic one-dimensional and two-dimensional data clearly demonstrate the advantage of our solution in comparison with the γ-normalized Laplacian approach (Linderberg, 1998) and the standard sample estimation approach (Matei, 2001). A quasi-real-time three-dimensional nodule characterization system is developed using this framework and validated with two clinical data sets of thin-section chest CT images. Our experiments with 1310 nodules resulted in 1) robustness against intraoperator and interoperator variability due to varying marker locations, 2) 81% correct estimation rate, 3) 3% false acceptance and 5% false rejection rates, and 4) correct characterization of clinically significant nonsolid ground-glass opacity nodules. This system processes each 33-voxel volume-of-interest by an average of 2 s with a 2.4-GHz Intel CPU. Our solution is generic and can be applied for the analysis of blob-like structures in various other applications.
Keywords :
Gaussian processes; computerised tomography; image segmentation; medical image processing; statistical analysis; /spl gamma/-normalized Laplacian approach; analytical volumetric measurement solutions; chi-square-based statistical verification; clinically significant nonsolid ground-glass opacity nodules; ellipsoidal boundary approximation; ellipsoidal geometrical structure; interoperator variability; intraoperator variability; linear scale-space theory; marker location variability; multiscale joint segmentation; multislice X-ray computed tomography; pulmonary nodules; quasireal-time three-dimensional nodule characterization system; robust anisotropic Gaussian fitting; robust mean shift-based analysis; robust statistical estimation; sample estimation approach; thin-section chest CT images; volumetric characterization; Anisotropic magnetoresistance; Cancer; Computed tomography; Image analysis; Image resolution; Image segmentation; Laplace equations; Robustness; Volume measurement; X-ray imaging; Anisotropic scale-space; Chi-square verification; Gaussian model fitting; covariance estimation; mean shift; multiscale analysis; multislice X-ray CT image analysis; part- and nonsolid nodules; pulmonary nodule characterization and segmentation; robust estimation; Algorithms; Anisotropy; Artificial Intelligence; Cluster Analysis; Coin Lesion, Pulmonary; Computer Simulation; Humans; Imaging, Three-Dimensional; Information Storage and Retrieval; Lung Neoplasms; Models, Biological; Models, Statistical; Normal Distribution; Observer Variation; Pattern Recognition, Automated; Radiographic Image Enhancement; Radiographic Image Interpretation, Computer-Assisted; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Subtraction Technique; Tomography, Spiral Computed;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2004.843172
Filename :
1397828
Link To Document :
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