README.md

---
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