--- license: cc-by-4.0 task_categories: - image-segmentation tags: - medical - retinal - fundus - vessel-segmentation - ophthalmology - glaucoma - diabetic-retinopathy pretty_name: HRF (High-Resolution Fundus) Image Database size_categories: - n<1K --- # HRF — High-Resolution Fundus Image Database Manual **retinal blood-vessel segmentation** ground truth for **45 high-resolution color fundus photographs** (3504×2336) from the Pattern Recognition Lab (CS5) and Dept. of Ophthalmology, **FAU Erlangen-Nürnberg**, with Brno University of Technology and the Eye Clinic Zlín — Budai et al., *Int. J. Biomedical Imaging* 2013. - **Modality:** color fundus photography (2D RGB), 3504×2336 - **Organ:** retina / eye - **Target:** binary blood-vessel segmentation - **Cases:** 45 — 15 healthy · 15 glaucomatous · 15 diabetic retinopathy - **License:** CC BY 4.0 - **Source:** [FAU HRF homepage](https://www5.cs.fau.de/research/data/fundus-images/) (official, author-hosted) > **Scope — please read.** This repository contains only the **segmentation** > component of HRF. The **36-image Image Quality Assessment set** hosted on the > same FAU page (18 good/bad pairs, different resolutions, *no* segmentation > ground truth, separate citation — Köhler et al., CBMS 2013) is **not** included: > it is a different dataset that shares a download page. A naive "download HRF" > yields 45+36 = 81 images; this repo is the 45 segmentation images. ## Splits **There is no official train/test split.** Neither the FAU page nor the Budai paper defines one, so all 45 cases are published as a single `train` split. The split most often seen in the literature is a *community* convention from **Orlando et al. 2017** (IEEE TBME 64(1):16–27) — the first 5 of each diagnosis category for training (15) and the remaining 30 for testing. It is **not** author-defined. Reconstruct it exactly with `case_index <= 5` (train) vs `case_index >= 6` (test). ## Columns | Column | Type | Notes | |---|---|---| | `image_id` | string | Case stem, e.g. `01_h`, `07_g`, `15_dr` — the pairing key | | `image` | Image (RGB) | 3504×2336 fundus photograph. **Original JPEG bytes, stored verbatim** (no re-encode) | | `mask` | Image (L) | **Ground truth.** Manual binary vessel segmentation, `{0, 255}`, 3504×2336 | | `fov_mask` | Image (L) | Field-of-view / camera-aperture mask, `{0, 255}`. *Auxiliary — not a segmentation target* | | `subset` | string | `healthy` \| `glaucoma` \| `diabetic_retinopathy` | | `case_index` | int32 | 1–15 within the subset (parsed from the filename prefix) | | `vessel_fraction` | float32 | Fraction of pixels labelled vessel (0.051–0.106, mean 0.077) | | `fov_fraction` | float32 | Fraction of pixels inside the FOV (~0.845) | | `od_center_x_a`, `od_center_y_a` | int32 | Optic-disc ("papilla") centre, **Expert A** | | `od_vessel_origin_x_a`, `od_vessel_origin_y_a` | int32 | Central-vessel origin, Expert A | | `od_diameter_a` | int32 | Optic-disc diameter in px, Expert A | | `od_center_x_b`, `od_center_y_b` | int32 | Optic-disc centre, **Expert B** | | `od_vessel_origin_x_b`, `od_vessel_origin_y_b` | int32 | Central-vessel origin, Expert B | | `od_diameter_b` | int32 | Optic-disc diameter in px, Expert B | For binary vessel segmentation use `mask > 0` (masks are already clean two-valued). **The optic-disc columns are coordinates, not masks.** FAU ships an `optic_disk_centers.xls` "Optic Disk Goldstandard" that contains *localisation* annotations only — there is no optic-disc segmentation in HRF. They are carried here as metadata for localisation/registration use. ## Ground truth `mask` is the manual vessel segmentation from `manual1/`. It is the only tier the Budai paper evaluates against (Tables 4–5, per-subset Se/Sp/Acc), and the FAU page describes it as produced by "a group of experts working in the field of retinal image analysis and clinicians from the cooperated ophthalmology clinics" — a single expert-consensus set, so there is no rater to choose. **HRF has no second-observer segmentation.** (The Budai paper's remark about "a second manual segmentation made by a human observer" refers to DRIVE and STARE, the external databases it compares against — not to HRF.) ### Mask normalisation applied here 42 of the 45 source vessel masks are strictly `{0, 255}`. Three — `11_h`, `12_h`, `13_h` — carry anti-aliased grey edges (175/215/203 distinct values); they are exactly the three files stored *uncompressed* rather than PackBits upstream, i.e. a different export path. Affected pixels: 599 / 1584 / 1119 out of 8,185,344 (0.007–0.019%). All masks were binarised at **≥128** so every case ships clean `{0, 255}`. The ≥128 threshold (rather than `>0`) keeps those three geometrically consistent with the other 42 instead of gaining a ~1 px anti-alias fringe. FOV masks are stored 3-channel RGB (R==G==B) at the source; they are reduced to single-channel here. ## Provenance, naming and cross-dataset overlap - **Provenance:** official, author-hosted FAU archives (`all.zip`, 76,317,613 B, byte-size verified). Counts match the paper exactly: 15/15/15 = 45. ⚠️ Third-party re-hosts vary in fidelity — one HF mirror carries 90 downscaled rows with no vessel GT. This repo is built from the FAU originals. - **Faithful naming:** yes, with the IQA-set exclusion noted in the scope box above. - **Ground-truth tier:** single expert-consensus vessel set; no second observer. - **Cross-dataset overlap: NONE.** HRF shares no images, archives or lineage with DRIVE, STARE, CHASE_DB1, IDRiD, PAPILA, RAVIR, RITE or Messidor — different acquisition sites, cameras and resolutions. No cross-reference ID exists or is needed. (Note that *third-party re-annotations* of these same 45 images do exist — e.g. HRF-AV artery/vein labels, HRF-Seg+ — and overlap HRF 100%; do not benchmark those alongside this repo.) ## Citation ```bibtex @article{budai2013robust, title = {Robust Vessel Segmentation in Fundus Images}, author = {Budai, Attila and Bock, R{\"u}diger and Maier, Andreas and Hornegger, Joachim and Michelson, Georg}, journal = {International Journal of Biomedical Imaging}, volume = {2013}, pages = {154860}, year = {2013}, doi = {10.1155/2013/154860} } ``` Companion database paper: Odstrcilik, J. et al. "Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database." *IET Image Processing* 7(4):373–383, 2013. doi:10.1049/iet-ipr.2012.0455