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Proceedings Paper

Seamless insertion of real pulmonary nodules in chest CT exams
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Paper Abstract

The availability of large medical image datasets is critical in many applications such as training and testing of computer aided diagnosis (CAD) systems, evaluation of segmentation algorithms, and conducting perceptual studies. However, collection of large repositories of clinical images is hindered by the high cost and difficulties associated with both the accumulation of data and establishment of the ground truth. To address this problem, we are developing an image blending tool that allows users to modify or supplement existing datasets by seamlessly inserting a real lesion extracted from a source image into a different location on a target image. In this study we focus on the application of this tool to pulmonary nodules in chest CT exams. We minimize the impact of user skill on the perceived quality of the blended image by limiting user involvement to two simple steps: the user first draws a casual boundary around the nodule of interest in the source, and then selects the center of desired insertion area in the target. We demonstrate examples of the performance of the proposed system on samples taken from the Lung Image Database Consortium (LIDC) dataset, and compare the noise power spectrum (NPS) of blended nodules versus that of native nodules in simulated phantoms.

Paper Details

Date Published: 27 March 2014
PDF: 10 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90351K (27 March 2014); doi: 10.1117/12.2043786
Show Author Affiliations
Aria Pezeshk, U.S. Food and Drug Administration (United States)
Berkman Sahiner, U.S. Food and Drug Administration (United States)
Rongping Zeng, U.S. Food and Drug Administration (United States)
Adam Wunderlich, U.S. Food and Drug Administration (United States)
Weijie Chen, U.S. Food and Drug Administration (United States)
Nicholas Petrick, U.S. Food and Drug Administration (United States)

Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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