ArticleOptics express2017
Frequency-constrained robust principal component analysis: a sparse representation approach to segmentation of dynamic features in optical coherence tomography imaging.
Article in Optics express, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Using Principal Components Analysis to Visualize Motion and Mitigate Artifacts in Dynamic Optical Coherence Tomography.Journal of biophotonics · 2026Article
- Neural-network based high-speed volumetric dynamic optical coherence tomography.Biomedical optics express · 2024Article
- Label-free intratissue activity imaging of alveolar organoids with dynamic optical coherence tomography.Biomedical optics express · 2023Article
- Dynamic volumetric imaging and cilia beat mapping in the mouse male reproductive tract with optical coherence tomography.Biomedical optics express · 2022Article
- Noise reduction by adaptive-SIN filtering for retinal OCT images.Scientific reports · 2021Article
- 3-D compressed sensing optical coherence tomography using predictive coding.Biomedical optics express · 2021Article
- Fast esophageal layer segmentation in OCT images of guinea pigs based on sparse Bayesian classification and graph search.Biomedical optics express · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Sparse representation theory is an exciting area of research with recent applications in medical imaging and detection, segmentation, and quantitative analysis of biological processes. We present a variant on the robust-principal component analysis (RPCA) algorithm, called frequency constrained RPCA (FC-RPCA), for selectively segmenting dynamic phenomena that exhibit spectra within a user-defined range of frequencies. The algorithm lacks subjective parameter tuning and demonstrates robust segmentation in datasets containing multiple motion sources and high amplitude noise. When tested on 17 ex-vivo, time lapse optical coherence tomography (OCT) B-scans of human ciliated epithelium, segmentation accuracies ranged between 91-99% and consistently out-performed traditional RPCA.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.