Robust anisotropic Gaussian fitting for volumetric characterization of pulmonary nodules in multislice CT.
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 gamma-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.
Okada
Kazunori
K
Real-Time Vision and Modeling Department, Siemens Corporate Research, 755 College Road East, Princeton, NJ 08540, USA. Kazunori.okada@siemens.com
Comaniciu
Dorin
D
Krishnan
Arun
A
D000465:Algorithms; D016880:Anisotropy; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008175:Lung Neoplasms / Q000000981:diagnostic imaging; D008954:Models, Biological; D015233:Models, Statistical; D016011:Normal Distribution; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D013382:Subtraction Technique; D036542:Tomography, Spiral Computed / Q000379:methods*