Original investigationClassification of Parenchymal Abnormality in Scleroderma Lung Using a Novel Approach to Denoise Images Collected via a Multicenter Study1
Section snippets
Materials and methods
The use of the anonymous data set from Scleroderma Lung Study (16) was approved by our local institutional review board.
Results
Representative results of implementing Aujol's algorithm to denoise CT image data are shown in Figure 4. The image pairs correspond to the original and denoised examples of abnormalities (LF, GG, and HC) and NL shown in Figure 3. In original images, the noise parameter equaled 50, which was the upper bound of SD in the aorta across all patients.
From the training set, our logistic model selected 45 and 38 texture features from original and denoised images, respectively, as significant predictors
Discussion
Computational approaches have been used to classify disease patterns in the lung, but none has investigated the influence of noise on classification accuracy. To date, most classification models are based on image data sets derived from a single platform and thus, may not be generalizable to images acquired on different platforms or to using different imaging protocols even on the same platform (3, 4, 5, 6, 7). CT image noise can be influenced by the technique used: the product of tube current
Conclusion
Accurate and robust computerized classifications based on texture features appear improved when using denoised images. This is particularly important to discriminate normal from abnormal parenchyma given that GG and other disease patterns can be mimicked by image noise, leading to false positives. Based on this initial work that has a limited number of disease types, our CT denoising method yielded higher rates of correct NL tissue classification in scleroderma patients. Texture features from a
Acknowledgments
The authors appreciate the invaluable guidance of Jean-Francois Aujol on denoise and its implementation. We also thank David Qing for programming of the contouring tools, Irene da Costa, Project Manager, Radiology, SLS, and Laura Guzman for editing and proofreading this manuscript.
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Support provided by Public Health Service grants from the National Heart Lung and Blood Institute, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Center for Research Resources of the National Institutes of Health, Bethesda, MD, and Grants U01 HL60587-01A1 and R01 HL072424 from the National Institutes of Health. Cyclophosphamide (Cytoxan®) was supplied by Bristol-Myers Squibb, Princeton, NJ.