Open AccessPhase distribution analysis of tissues based on the off-axis digital holographic hybrid reconstruction algorithm
Author(s) -
Yunyi Lin,
Liang Dong,
Haige Chen,
Sujuan Huang
Publication year - 2017
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.9.000001
Subject(s) - aliasing , digital holography , algorithm , computer science , holography , histogram , hybrid algorithm (constraint satisfaction) , angular spectrum method , phase (matter) , anti aliasing , frequency domain , artificial intelligence , computer vision , optics , digital signal processing , image (mathematics) , physics , diffraction , constraint satisfaction , quantum mechanics , undersampling , probabilistic logic , constraint logic programming , audio signal processing , audio signal , computer hardware
Off-axis digital holography (DH) has great potential in histopathology for its high efficiency and precision. Phase distribution, usually extracted by the angular spectrum (AS) algorithm from a digital hologram, reflects important structural information of biological tissues. However, the complex structure of tissues introduces spectrum aliasing of the hologram, making the AS algorithm hard to realize and accurate phase analysis difficult to conduct. Here, we present a hybrid reconstruction algorithm, combining Fresnel reconstruction in spatial domain with the AS algorithm in frequency domain, to solve aliasing by spatial filtering. Through simulation, we demonstrate the feasibility and superiority of the hybrid algorithm and verified the precision (10 -3 rad) of the hybrid algorithm with spectrum aliasing. We extract phase distributions from normal urothelial and bladder cancer tissues by the hybrid algorithm and make quantitative analysis through histogram and standard deviation. The result shows the hybrid algorithm in off-axis DH has great advantage for the high-precision phase extraction of tissues and supplies significant information for cancer diagnosis.