Prof. Ye Xu Profile

Prof. Ye Xu

at Univ of Iowa Hospitals and Clinics

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Author

Publications (3)

Proceedings Article | 13 March 2006 Paper

Effect of mixing scanner types and reconstruction kernels on the characterization of lung parenchymal pathologies: emphysema, interstitial pulmonary fibrosis and normal non-smokers

Ye Xu, Edwin van Beek, Geoffrey McLennan, Junfeng Guo, Milan Sonka, Eric Hoffman

Proceedings Volume 6143, 61430M (2006) https://doi.org/10.1117/12.657595

KEYWORDS: Scanners, Emphysema, Lung, Computed tomography, Image classification, Spatial resolution, Pulmonary fibrosis, Pathology, Convolution, Tissues

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In this study we utilize our texture characterization software (3-D AMFM) to characterize interstitial lung diseases (including emphysema) based on MDCT generated volumetric data using 3-dimensional texture features. We have sought to test whether the scanner and reconstruction filter (kernel) type affect the classification of lung diseases using the 3-D AMFM. We collected MDCT images in three subject groups: emphysema (n=9), interstitial pulmonary fibrosis (IPF) (n=10), and normal non-smokers (n=9). In each group, images were scanned either on a Siemens Sensation 16 or 64-slice scanner, (B50f or B30 recon. kernel) or a Philips 4-slice scanner (B recon. kernel). A total of 1516 volumes of interest (VOIs; 21x21 pixels in plane) were marked by two chest imaging experts using the Iowa Pulmonary Analysis Software Suite (PASS). We calculated 24 volumetric features. Bayesian methods were used for classification. Images from different scanners/kernels were combined in all possible combinations to test how robust the tissue classification was relative to the differences in image characteristics. We used 10-fold cross validation for testing the result. Sensitivity, specificity and accuracy were calculated. One-way Analysis of Variances (ANOVA) was used to compare the classification result between the various combinations of scanner and reconstruction kernel types. This study yielded a sensitivity of 94%, 91%, 97%, and 93% for emphysema, ground-glass, honeycombing, and normal non-smoker patterns respectively using a mixture of all three subject groups. The specificity for these characterizations was 97%, 99%, 99%, and 98%, respectively. The F test result of ANOVA shows there is no significant difference (p <0.05) between different combinations of data with respect to scanner and convolution kernel type. Since different MDCT and reconstruction kernel types did not show significant differences in regards to the classification result, this study suggests that the 3-D AMFM can be generally introduced.

Proceedings Article | 14 April 2005 Paper

Sensitivity and specificity of 3-D texture analysis of lung parenchyma is better than 2-D for discrimination of lung pathology in stage 0 COPD

Ye Xu, Milan Sonka, Geoffrey McLennan, Junfeng Guo, Eric Hoffman

Proceedings Volume 5746, (2005) https://doi.org/10.1117/12.595879

KEYWORDS: Lung, Emphysema, Chronic obstructive pulmonary disease, Tissues, Pathology, Computed tomography, Fractal analysis, 3D image processing, Statistical analysis, Image classification

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Proceedings Article | 13 July 2004 Paper

A whole organ serial sectioning and imaging system for correlation of pathology to computer tomography

Jessica de Ryk, Eman Namati, Joseph Reinhardt, Christopher Piker, Ye Xu, Le Liu, Eric Hoffman, Geoffrey McLennan

Proceedings Volume 5324, (2004) https://doi.org/10.1117/12.529137

KEYWORDS: Computed tomography, Pathology, Image registration, Tissues, Lung, Algorithm development, Image processing, Composites, Natural surfaces, Imaging systems

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