Diagnosis of lung nodule using semivariogram and geometric measures in computerized tomography images

This paper uses the geostatistical function - semivariogram and a set of 3D geometric measures - sphericity index, convexity index, extrinsic and intrinsic curvature index and surface type, to characterize lung nodules as malignant or benign in computerized tomography images. Based on a sample of 31 nodules, 25 benign and 6 malignant, these methods are first analyzed individually and then jointly, with techniques for classification and analysis (stepwise discriminant analysis, leave-one-out and ROC curve). We have concluded that the individual measures and their combinations produce good results in the diagnosis of lung nodules.

[1]  Nick C. Fox,et al.  MR image texture analysis applied to the diagnosis and tracking of Alzheimer's disease , 1998, IEEE Transactions on Medical Imaging.

[2]  David W. Henderson,et al.  Differential Geometry: A Geometric Introduction , 1997 .

[3]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[4]  M. McNitt-Gray,et al.  A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: preliminary results. , 1999, Medical physics.

[5]  M. Greiner,et al.  Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. , 2000, Preventive veterinary medicine.

[6]  Vassili A. Kovalev,et al.  3D Texture Analysis of MRI Brain Datasets , 2001, IEEE Trans. Medical Imaging.

[7]  O S Miettinen,et al.  Early lung cancer action project: a summary of the findings on baseline screening. , 2001, The oncologist.

[8]  David A. Clunie,et al.  DICOM Structured Reporting , 2000 .

[9]  P. Pattynama,et al.  Receiver operating characteristic (ROC) analysis: basic principles and applications in radiology. , 1998, European journal of radiology.

[10]  R. Ferreyra,et al.  Reduction of soil water spatial sampling density using scaled semivariograms and simulated annealing , 2002 .

[11]  Clayton V. Deutsch,et al.  GSLIB: Geostatistical Software Library and User's Guide , 1993 .

[12]  D. V. van Essen,et al.  Structural and Functional Analyses of Human Cerebral Cortex Using a Surface-Based Atlas , 1997, The Journal of Neuroscience.

[13]  M. McNitt-Gray,et al.  The effects of co-occurrence matrix based texture parameters on the classification of solitary pulmonary nodules imaged on computed tomography. , 1999, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[14]  C. J. Huberty,et al.  Applied Discriminant Analysis , 1994 .

[15]  Jan J. Koenderink,et al.  Solid shape , 1990 .

[16]  Noboru Niki,et al.  Classification of pulmonary nodules in thin-section CT images based on shape characterization , 1997, Proceedings of International Conference on Image Processing.

[17]  Jean-Philippe Gastellu-Etchegorry,et al.  Sensitivity of Texture of High Resolution Images of Forest to Biophysical and Acquisition Parameters , 1998 .

[18]  W J Kostis,et al.  Computer-aided diagnosis for lung cancer. , 2000, Radiologic clinics of North America.

[19]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .