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Enhanced Optical Coherence Tomography Imaging Using a Histogram-Based Denoising Algorithm
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- Authors
- Keo-Sik Kim, Hyoung-Jun Park, Hyun Seo Kang
- Issue Date
- 2015-11
- Citation
- Optical Engineering, v.54, no.11, pp.1-4
- ISSN
- 0091-3286
- Publisher
- SPIE
- Language
- English
- Type
- Journal Article
- Abstract
- A histogram-based denoising algorithm was developed to effectively reduce ghost artifact noise and enhance the quality of an optical coherence tomography (OCT) imaging system used to guide surgical instruments. The noise signal is iteratively detected by comparing the histogram of the ensemble average of all A-scans, and the ghost artifacts included in the noisy signal are removed separately from the raw signals using the polynomial curve fitting method. The devised algorithm was simulated with various noisy OCT images, and >87% of the ghost artifact noise was removed despite different locations. Our results show the feasibility of selectively and effectively removing ghost artifact noise.
- KSP Keywords
- Curve fitting method, Denoising algorithm, Different locations, Ensemble average, Histogram-Based, OCT images, Optical Coherence Tomography, Polynomial curve fitting, Surgical instruments, Tomography imaging, imaging system