TY - GEN
T1 - Refocusing phase contrast microscopy images
AU - Han, Liang
AU - Yin, Zhaozheng
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Phase contrast microscopy is a very popular non-invasive technique for monitoring live cells. However, its images can be blurred if optics are imperfectly aligned and the visualization on specimen details can be affected by noisy background. We propose an effective algorithm to refocus phase contrast microscopy images from two perspectives: optics and specimens. First, given a defocused image caused by misaligned optics, we estimate the blur kernel based on the sparse prior of dark channel, and non-blindly refocus the image with the hyper-Laplacian prior of image gradients. Then, we further refocus the image contents on specimens by removing the artifacts from the background, which provides a sharp visualization on fine specimen details. The proposed algorithm is both qualitatively and quantitatively evaluated on a dataset of 500 phase contrast microscopy images, showing its superior performance for visualizing specimens and facilitating microscopy image analysis.
AB - Phase contrast microscopy is a very popular non-invasive technique for monitoring live cells. However, its images can be blurred if optics are imperfectly aligned and the visualization on specimen details can be affected by noisy background. We propose an effective algorithm to refocus phase contrast microscopy images from two perspectives: optics and specimens. First, given a defocused image caused by misaligned optics, we estimate the blur kernel based on the sparse prior of dark channel, and non-blindly refocus the image with the hyper-Laplacian prior of image gradients. Then, we further refocus the image contents on specimens by removing the artifacts from the background, which provides a sharp visualization on fine specimen details. The proposed algorithm is both qualitatively and quantitatively evaluated on a dataset of 500 phase contrast microscopy images, showing its superior performance for visualizing specimens and facilitating microscopy image analysis.
UR - https://www.scopus.com/pages/publications/85029539995
U2 - 10.1007/978-3-319-66185-8_8
DO - 10.1007/978-3-319-66185-8_8
M3 - Conference contribution
AN - SCOPUS:85029539995
SN - 9783319661841
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 65
EP - 74
BT - Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
A2 - Jannin, Pierre
A2 - Duchesne, Simon
A2 - Descoteaux, Maxime
A2 - Franz, Alfred
A2 - Collins, D. Louis
A2 - Maier-Hein, Lena
PB - Springer Verlag
T2 - 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Y2 - 11 September 2017 through 13 September 2017
ER -