Improvement of Image Characteristics in Digital Breast Tomosynthesis by Incorporating a Compressed-sensing (CS) Deblurring Framework: Simulation Study

DOI

10.2991/bst-16.2016.28How to use a DOI?

Keywords

Digital breast tomosynthesis, Deblurring, Compressed-sensing.

Abstract

In this work, we considered a compressed-sensing (CS)-based framework with the total-variation regularization penalty for image deblurring of high accuracy in digital breast tomosynthesis (DBT). We implemented the proposed algorithm and performed a systematic simulation to demonstrate its viability for improving the image characteristics in DBT. In the simulation, blurred noisy projection images of a 3D breast phantom were generated by convolving their original (or exact) version by a 2D Gaussian blur kernel ( = 2 in pixel unit, kernel size = 11 × 11), followed by adding Gaussian noise (m = 0, 2 = 0.05), and deblurred by using the proposed algorithm before performing DBT reconstruction.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Heemoon Cho
AU  - Kyuseok Kim
AU  - Hyosung Cho
AU  - Hyunwoo Lim
AU  - Yeonok Park
PY  - 2016/01
DA  - 2016/01
TI  - Improvement of Image Characteristics in Digital Breast Tomosynthesis by Incorporating a Compressed-sensing (CS) Deblurring Framework: Simulation Study
BT  - Proceedings of the 2016 International Conference on Biological Sciences and Technology
PB  - Atlantis Press
SP  - 183
EP  - 186
SN  - 2468-5747
UR  - https://doi.org/10.2991/bst-16.2016.28
DO  - 10.2991/bst-16.2016.28
ID  - Cho2016/01
ER  -