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Record W2142095525 · doi:10.1109/iembs.2009.5335389

Detection of the temporal arcade in fundus images of the retina using the hough transform

2009· article· en· W2142095525 on OpenAlex
Faraz Oloumi, Rangaraj M. Rangayyan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicRetinal Imaging and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHough transformRetinaComputer scienceArtificial intelligenceFundus (uterus)Computer visionParametric statisticsTortuosityPattern recognition (psychology)Image (mathematics)OpticsOphthalmologyMathematicsMedicinePhysicsMaterials science

Abstract

fetched live from OpenAlex

Quantitative analysis of the vascular architecture of the retina can help in monitoring the effects of retinopathy on the visual system. Retinopathy affects the blood vessels in the retina through modification of the shape, width, tortuosity, and the angle of insertion of the temporal arcade. Monitoring the openness of the temporal arcade and changes with treatment can facilitate improved diagnosis and optimized treatment. We propose methods for the detection and parametric modeling of the temporal arcade, including gradient operators and Gabor functions to detect retinal vessels, and the Hough transform to detect parabolic forms. Results obtained with 40 images of the retina indicate accurate to acceptable results for 24 images and partial fits of the parabolic models for 11 images.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.108

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.287
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations13
Published2009
Admission routes1
Has abstractyes

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