| --- |
| 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 |
| |
