Robust anisotropic Gaussian fitting for volumetric characterization of Pulmonary nodules in multislice CT

• Published in: IEEE transactions on medical imaging Vol. 24; no. 3; pp. 409 - 423
• Main Authors: Okada, K., Comaniciu, D., Krishnan, A.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2005; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anisotropic magnetoresistance; Anisotropic scale-space; Anisotropy; Artificial Intelligence; Cancer; Chi-square verification; Cluster Analysis; Computed tomography; Computer Simulation; covariance estimation; Gaussian model fitting; Humans; Image analysis; Image resolution; Image segmentation; Imaging, Three-Dimensional - methods; Information Storage and Retrieval - methods; Laplace equations; Lung Neoplasms - diagnostic imaging; mean shift; Models, Biological; Models, Statistical; multiscale analysis; multislice X-ray CT image analysis; Normal Distribution; Observer Variation; part- and nonsolid nodules; Pattern Recognition, Automated - methods; pulmonary nodule characterization and segmentation; Radiographic Image Enhancement - methods; Radiographic Image Interpretation, Computer-Assisted - methods; Reproducibility of Results; robust estimation; Robustness; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Solitary Pulmonary Nodule - diagnostic imaging; Studies; Subtraction Technique; Tomography, Spiral Computed - methods; Volume measurement; X-ray imaging
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2004.843172

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 /spl 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.