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 /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.

[1]  L. Schwartz,et al.  Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm , 2003, Journal of applied clinical medical physics.

[2]  Shoji Kido,et al.  Fractal Analysis of Internal and Peripheral Textures of Small Peripheral Bronchogenic Carcinomas in Thin-section Computed Tomography: Comparison of Bronchioloalveolar Cell Carcinomas With Nonbronchioloalveolar Cell Carcinomas , 2003, Journal of computer assisted tomography.

[3]  Takashi Ohtsuka,et al.  Radiological examination for peripheral lung cancers and benign nodules less than 10 mm. , 2003, Lung cancer.

[4]  Martin Fiebich,et al.  Automatic detection of pulmonary nodules at spiral CT: clinical application of a computer-aided diagnosis system , 2002, European Radiology.

[5]  Deborah Schrag,et al.  Annual report to the nation on the status of cancer, 1975-2002, featuring population-based trends in cancer treatment. , 2005, Journal of the National Cancer Institute.

[6]  R. Manmatha,et al.  Scale Space Technique for Word Segmentation in Handwritten Documents , 1999, Scale-Space.

[7]  Kunio Doi,et al.  Computer-aided diagnosis in chest radiography , 2007, Comput. Medical Imaging Graph..

[8]  W. Heindel,et al.  Volumetric measurements of pulmonary nodules at multi-row detector CT: in vivo reproducibility , 2003, European Radiology.

[9]  Shinji Yamamoto,et al.  Recognition of lung nodules from x-ray CT images using 3D Markov random field models , 2002, SPIE Medical Imaging.

[10]  Sabine Van Huffel,et al.  Total least squares problem - computational aspects and analysis , 1991, Frontiers in applied mathematics.

[11]  D. Naidich,et al.  Lung nodule detection and characterization with multislice CT. , 2003, Radiologic clinics of North America.

[12]  Kenichi Kanatani,et al.  Do we really have to consider covariance matrices for image features? , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[13]  O. Miettinen,et al.  CT screening for lung cancer: frequency and significance of part-solid and nonsolid nodules. , 2002, AJR. American journal of roentgenology.

[14]  Noboru Niki,et al.  Computerized analysis of pulmonary nodules in topological and histogram feature spaces , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[15]  F. Fischbach,et al.  Detection of pulmonary nodules by multislice computed tomography: improved detection rate with reduced slice thickness , 2003, European Radiology.

[16]  P. Meer,et al.  Heteroscedastic errors-in-variables models in computer vision , 2001 .

[17]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[19]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[21]  A. Jemal,et al.  Annual report to the nation on the status of cancer, 1975-2000, featuring the uses of surveillance data for cancer prevention and control. , 2003, Journal of the National Cancer Institute.

[22]  Keinosuke Fukunaga,et al.  Statistical Pattern Recognition , 1993, Handbook of Pattern Recognition and Computer Vision.

[23]  Yixin Chen,et al.  Estimating symmetric, positive definite matrices in robotic control , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[24]  J. Koenderink.  The structure of images , 2004, Biological Cybernetics.

[25]  Margrit Betke,et al.  Small pulmonary nodules: volume measurement at chest CT--phantom study. , 2003, Radiology.

[26]  William H. Press,et al.  Numerical recipes in C , 2002 .

[27]  Abhir Bhalerao,et al.  Estimating Local and Global Image Structure using a Gaussian Intensity Model , 2001 .

[28]  Max A. Viergever,et al.  Computer-aided diagnosis in chest radiography: a survey , 2001, IEEE Transactions on Medical Imaging.

[29]  Gene H. Golub,et al.  Matrix computations , 1983 .

[30]  Guillermo Sapiro,et al.  Robust anisotropic diffusion , 1998, IEEE Trans. Image Process..

[31]  Rachid Deriche,et al.  Regularization, Scale-Space, and Edge Detection Filters , 1996, ECCV.

[32]  A. Reeves,et al.  Two-dimensional multi-criterion segmentation of pulmonary nodules on helical CT images. , 1999, Medical physics.

[33]  Hiroshi Fujita,et al.  Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique , 2001, IEEE Transactions on Medical Imaging.

[34]  Dorin Comaniciu,et al.  Robust 3D Segmentation of Pulmonary Nodules in Multislice CT Images , 2004, MICCAI.

[35]  Andrew P. Witkin,et al.  Scale-Space Filtering , 1983, IJCAI.

[36]  Dorin Comaniciu,et al.  An Algorithm for Data-Driven Bandwidth Selection , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[37]  W J Kostis,et al.  Computer-aided diagnosis of small pulmonary nodules. , 2000, Seminars in ultrasound, CT, and MR.

[38]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[39]  Michael F. McNitt-Gray,et al.  Patient-specific models for lung nodule detection and surveillance in CT images , 2001, IEEE Transactions on Medical Imaging.

[40]  Andrew P. Witkin,et al.  Scale-space filtering: A new approach to multi-scale description , 1984, ICASSP.

[41]  Peter J. Rousseeuw,et al.  Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.

[42]  H. Hu.  Positive definite constrained least-squares estimation of matrices , 1995 .

[43]  Mads Nielsen,et al.  Feature-Based Image Analysis , 2003, International Journal of Computer Vision.

[44]  Anthony P. Reeves,et al.  Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images , 2003, IEEE Transactions on Medical Imaging.