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Proceedings Paper

Effect of denoising on supervised lung parenchymal clusters
Author(s): Padmapriya Jayamani; Sushravya Raghunath; Srinivasan Rajagopalan; Ronald A. Karwoski; Brian J. Bartholmai; Richard A. Robb
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Paper Abstract

Denoising is a critical preconditioning step for quantitative analysis of medical images. Despite promises for more consistent diagnosis, denoising techniques are seldom explored in clinical settings. While this may be attributed to the esoteric nature of the parameter sensitve algorithms, lack of quantitative measures on their ecacy to enhance the clinical decision making is a primary cause of physician apathy. This paper addresses this issue by exploring the eect of denoising on the integrity of supervised lung parenchymal clusters. Multiple Volumes of Interests (VOIs) were selected across multiple high resolution CT scans to represent samples of dierent patterns (normal, emphysema, ground glass, honey combing and reticular). The VOIs were labeled through consensus of four radiologists. The original datasets were ltered by multiple denoising techniques (median ltering, anisotropic diusion, bilateral ltering and non-local means) and the corresponding ltered VOIs were extracted. Plurality of cluster indices based on multiple histogram-based pair-wise similarity measures were used to assess the quality of supervised clusters in the original and ltered space. The resultant rank orders were analyzed using the Borda criteria to nd the denoising-similarity measure combination that has the best cluster quality. Our exhaustive analyis reveals (a) for a number of similarity measures, the cluster quality is inferior in the ltered space; and (b) for measures that benet from denoising, a simple median ltering outperforms non-local means and bilateral ltering. Our study suggests the need to judiciously choose, if required, a denoising technique that does not deteriorate the integrity of supervised clusters.

Paper Details

Date Published: 23 February 2012
PDF: 10 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83152X (23 February 2012); doi: 10.1117/12.911650
Show Author Affiliations
Padmapriya Jayamani, Mayo Clinic College of Medicine (United States)
Sushravya Raghunath, Mayo Clinic College of Medicine (United States)
Srinivasan Rajagopalan, Mayo Clinic College of Medicine (United States)
Ronald A. Karwoski, Mayo Clinic College of Medicine (United States)
Brian J. Bartholmai, Mayo Clinic College of Medicine (United States)
Richard A. Robb, Mayo Clinic College of Medicine (United States)

Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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