Seamless insertion of real pulmonary nodules in chest CT exams

Aria Pezeshk; Berkman Sahiner; Rongping Zeng; Adam Wunderlich; Weijie Chen; Nicholas Petrick

doi:10.1117/12.2043786

27 March 2014 Seamless insertion of real pulmonary nodules in chest CT exams

Aria Pezeshk, Berkman Sahiner, Rongping Zeng, Adam Wunderlich, Weijie Chen, Nicholas Petrick

Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 90351K (2014) https://doi.org/10.1117/12.2043786
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

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.

Citation Download Citation

Aria Pezeshk, Berkman Sahiner, Rongping Zeng, Adam Wunderlich, Weijie Chen, and Nicholas Petrick "Seamless insertion of real pulmonary nodules in chest CT exams", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90351K (27 March 2014); https://doi.org/10.1117/12.2043786

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available