ArticleIEEE transactions on image processing : a publication of the IEEE Signal Processing Society2016
The Radon Cumulative Distribution Transform and Its Application to Image Classification.
Article in IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Fast Rigid Alignment of Heterogeneous Images in Sliced Wasserstein Distance.SIAM journal on imaging sciences · 2026Article
- Local Sliced Wasserstein Feature Sets for Illumination Invariant Face Recognition.Pattern recognition · 2025Article
- A modulation format recognition and optical signal-to-noise ratio monitoring scheme based on residual network and Taylor score pruning.PloS one · 2025Article
- Impact of GAN artifacts for simulating mammograms on identifying mammographically occult cancer.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Invariance encoding in sliced-Wasserstein space for image classification with limited training data.Pattern recognition · 2023Article
- Predicting Malignancy of Breast Imaging Findings Using Quantitative Analysis of Contrast-Enhanced Mammography (CEM).Diagnostics (Basel, Switzerland) · 2023Article
- Identifying Women With Mammographically- Occult Breast Cancer Leveraging GAN-Simulated Mammograms.IEEE transactions on medical imaging · 2022Article
- Radon Cumulative Distribution Transform Subspace Modeling for Image Classification.Journal of mathematical imaging and vision · 2021Article
- PARTITIONING SIGNAL CLASSES USING TRANSPORT TRANSFORMS FOR DATA ANALYSIS AND MACHINE LEARNING.Sampling theory, signal processing, and data analysis · 2021Article
- Detecting mammographically occult cancer in women with dense breasts using deep convolutional neural network and Radon Cumulative Distribution Transform.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Discovery and visualization of structural biomarkers from MRI using transport-based morphometry.NeuroImage · 2018Article
- Learning to Predict Ischemic Stroke Growth on Acute CT Perfusion Data by Interpolating Low-Dimensional Shape Representations.Frontiers in neurology · 2018Article
- A TransportationJournal of mathematical imaging and vision · 2017Article
- Optimal Mass Transport: Signal processing and machine-learning applications.IEEE signal processing magazine · 2017Article
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
Invertible image representation methods (transforms) are routinely employed as low-level image processing operations based on which feature extraction and recognition algorithms are developed. Most transforms in current use (e.g., Fourier, wavelet, and so on) are linear transforms and, by themselves, are unable to substantially simplify the representation of image classes for classification. Here, we describe a nonlinear, invertible, low-level image processing transform based on combining the well-known Radon transform for image data, and the 1D cumulative distribution transform proposed earlier. We describe a few of the properties of this new transform, and with both theoretical and experimental results show that it can often render certain problems linearly separable in a transform space.
Indexed as
Identifiers
What OpenQuestion holds
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.