High resolution multidetector CT-aided tissue analysis and quantification of lung fibrosis.

Rational and Objectives Volumetric high-resolution scans can be acquired of the lungs with multi-detector CT (MDCT). Such scans have potential to facilitate useful visualization, characterization, and quantification of the extent of diffuse lung diseases, such as Usual Interstitial Pneumonitis or Idiopathic Pulmonary Fibrosis (UIP/IPF). There is a need to objectify, standardize and improve the accuracy and repeatability of pulmonary disease characterization and quantification from such scans. This paper presents a novel texture analysis approach toward classification and quantification of various pathologies present in lungs with UIP/IPF. The approach integrates a texture matching method with histogram feature analysis.

[1]  G. van Kaick,et al.  Usual interstitial pneumonia. Quantitative assessment of high-resolution computed tomography findings by computer-assisted texture-based image analysis. , 1997, Investigative radiology.

[2]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[3]  Yongmin Kim,et al.  A methodology for evaluation of boundary detection algorithms on medical images , 1997, IEEE Transactions on Medical Imaging.

[4]  W A Kalender,et al.  Standardized quantitative high resolution CT in lung diseases. , 1991, Journal of computer assisted tomography.

[5]  Eric A. Hoffman,et al.  Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images , 2001, IEEE Transactions on Medical Imaging.

[6]  E. Hall,et al.  Computer Classification of Pneumoconiosis from Radiographs of Coal Workers , 1975, IEEE Transactions on Biomedical Engineering.

[7]  G. W. Williams,et al.  Comparing the joint agreement of several raters with another rater. , 1976, Biometrics.

[8]  CT Attenuation Values of Lung Density in Sarcoidosis , 1983, Journal of computer assisted tomography.

[9]  J. Tooze,et al.  Predicting survival in idiopathic pulmonary fibrosis: scoring system and survival model. , 2001, American journal of respiratory and critical care medicine.

[10]  James V. Miller,et al.  Quantification of Emphysema Severity by Histogram Analysis of CT Scans , 2005, MICCAI.

[11]  Martha Elizabeth Shenton,et al.  Two Methods for Validating Brain Tissue Classifiers , 2005, MICCAI.

[12]  Peter J. Bickel,et al.  The Earth Mover's distance is the Mallows distance: some insights from statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[13]  G S Lodwick,et al.  Towards computer analysis of pulmonary infiltration. , 1978, Investigative radiology.

[14]  Milan Sonka,et al.  Automated segmentation of pulmonary vascular tree from 3D CT images , 2004, SPIE Medical Imaging.

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

[16]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[17]  R. Bellman.  Dynamic programming. , 1957, Science.

[18]  Ernest L. Hall,et al.  Texture Measures for Automatic Classification of Pulmonary Disease , 1972, IEEE Transactions on Computers.

[19]  N. Müller,et al.  "Density mask". An objective method to quantitate emphysema using computed tomography. , 1988, Chest.

[20]  Guido Gerig,et al.  3D Multi-scale line filter for segmentation and visualization of curvilinear structures in medical images , 1997, CVRMed.

[21]  Max A. Viergever,et al.  Normalized mutual information-based registration using K-means clustering-based histogram binning , 2003, SPIE Medical Imaging.

[22]  J A Merchant,et al.  High-resolution CT-derived measures of lung density are valid indexes of interstitial lung disease. , 1994, Journal of applied physiology.

[23]  Catherine Beigelman-Aubry,et al.  Multi-detector row CT and postprocessing techniques in the assessment of diffuse lung disease. , 2005, Radiographics : a review publication of the Radiological Society of North America, Inc.

[24]  Michael L Goris,et al.  An automated approach to quantitative air trapping measurements in mild cystic fibrosis. , 2003, Chest.

[25]  R. Kruger,et al.  Automated computer screening of chest radiographs for pneumoconiosis. , 1976, Investigative radiology.

[26]  Andrew Zisserman,et al.  A Statistical Approach to Texture Classification from Single Images , 2005, International Journal of Computer Vision.

[27]  Andrew Zisserman,et al.  Texture classification: are filter banks necessary? , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[28]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[29]  N L Müller,et al.  Chronic obstructive pulmonary disease • 4: Imaging the lungs in patients with chronic obstructive pulmonary disease , 2002, Thorax.

[30]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[31]  D. Aykac,et al.  Segmentation and analysis of the human airway tree from three-dimensional X-ray CT images , 2003, IEEE Transactions on Medical Imaging.

[32]  William B. Thompson,et al.  Computer Diagnosis of Pneumoconiosis , 1974, IEEE Trans. Syst. Man Cybern..

[33]  A. Nicholson,et al.  Idiopathic pulmonary fibrosis: a composite physiologic index derived from disease extent observed by computed tomography. , 2003, American journal of respiratory and critical care medicine.

[34]  William M. Wells,et al.  Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation , 2004, IEEE Transactions on Medical Imaging.

[35]  Hans-Ulrich Kauczor,et al.  Paired inspiratory/expiratory volumetric thin-slice CT scan for emphysema analysis: comparison of different quantitative evaluations and pulmonary function test. , 2005, Chest.

[36]  Ye Xu,et al.  MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies , 2006, IEEE Transactions on Medical Imaging.

[37]  Bram van Ginneken,et al.  Computer analysis of computed tomography scans of the lung: a survey , 2006, IEEE Transactions on Medical Imaging.