Improvement of Image Characteristics in Digital Breast Tomosynthesis by Incorporating a Compressed-sensing (CS) Deblurring Framework: Simulation Study
Heemoon Cho, Kyuseok Kim, Hyosung Cho, Hyunwoo Lim, Yeonok Park
Available Online January 2016.
- https://doi.org/10.2991/bst-16.2016.28How to use a DOI?
- Digital breast tomosynthesis, Deblurring, Compressed-sensing.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - The 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 - https://doi.org/10.2991/bst-16.2016.28 ID - Cho2016/01 ER -