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REVIEW 3 major objections 6 minor 36 references

Computationally Efficient Optic Nerve Head Detection in Retinal Fundus Images

T0 review · 3 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Radon transform plus window voting finds the optic nerve head in about 4 seconds per image, with 96–100% localization accuracy on three retinal image sets.

desk verdict Solid, clearly described ONH detector with respectable accuracy, but the Step 3 validation criterion is not as discriminative as claimed and the empirical comparison has unaddressed confounds. read the letter →

arxiv 1909.01558 v2 pith:DQQHIEFL submitted 2019-09-04 physics.med-ph eess.IV

Reza Pourreza-Shahri , Meysam Tavakoli , Nasser Kehtarnavaz This is my paper · ORCID
classification physics.med-pheess.IV
keywords opticnerveheaddetectionRadontransformfundusimagefluoresceinangiographydiabeticretinopathymulti-overlappingwindowscomputer-aideddiagnosisretinalanalysis
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that a fast optic nerve head detector can be built from two simple ingredients: the Radon transform and overlapping image windows. Because the optic nerve head is a bright, roughly circular disk, its Radon projections are nearly identical in all directions; the detector slides a window over the fundus image, keeps windows whose Radon peaks are strong, and picks the window with the least disagreement among projection profiles. On four test sets the reported hit rates are 100 percent (40 images), 96.3 percent (81), 97.5 percent (120 color), and 91.3 percent (120 angiography), and a STARE image is processed in about 4.1 seconds. The point of the speed is practical: if true, the method could serve as a cheap first step in retinal screening pipelines.

What carries the argument

The load-bearing object is the Radon transform paired with an MSE-over-projections validation rule. The Radon transform is the set of line integrals of image intensity taken along parallel beams at many angles; a bright circular disk produces a prominent, nearly angle-independent peak in this space. The paper adds a circular mask to each sub-image to remove the artificial diagonal bias of square windows, then uses the mean square error among the projection profiles as a similarity score: the sub-image whose projections agree best is declared to contain the ONH. All of the reported speed comes from keeping this machinery local, with small windows and an overlap step of 4, and from avoiding vessel segmentation entirely.

What would settle it

Run the algorithm on a set of fundus images in which a bright circular exudate or round illumination artifact lies away from the ONH and ask whether the reported center lands on the artifact; if even one such image is systematically selected over the ONH, the roundness-MSE criterion is not sufficient and the claimed accuracy depends on dataset-specific window sizes and thresholds.

Watch

Extended reading notes

Core claim

The central discovery is that optic nerve head localization can be posed as a roundness test on Radon projections rather than as vessel tracking or template matching. After masking out the non-fundus background and the window corners, the algorithm applies the Radon transform to each $n\times n$ sliding window, with window size set by the expected ONH diameter ($n=79$ for DRIVE, $n=130$ for STARE, $n=313$ for the high-resolution angiography set). In color images it uses the blue channel because yellowish structures like the ONH stand out there. A sub-image is kept as a candidate when its peak Radon projection exceeds 0.9 of the largest projection in that sub-image, and the final choice is the candidate that minimizes the mean square error among its projection profiles at different angles, relying on the property that a round object gives the same profile in every direction. The center of that winning window is reported as the ONH center, and the paper reports this procedure lands within 60 pixels of a manual reference in all 40 DRIVE images, 78 of 81 STARE images, 117 of 120 color fundus images, and 110 of 120 fluorescein angiography images.

Load-bearing premise

The method assumes the ONH is a round, bright object whose Radon profiles match in all directions, so any equally round, bright structure, such as an exudate or uneven round-shaped illumination, can win the MSE vote; the paper attributes its failures to the latter.

Editorial extensions

If this is right

  • Because the method avoids vessel segmentation, it can serve as an ONH locator in images where vessel extraction is costly or unreliable.
  • At roughly 4.1 seconds per STARE-size image, the detector is fast enough to run as a pre-filter before slower segmentation or classification steps.
  • Halving the image resolution preserved accuracy (100% DRIVE, 96.3% STARE, 95.9% for the color set) while cutting runtime by more than 12 times, so the method scales to high-resolution cameras.
  • The accuracy standard used throughout is placement within 60 pixels of the manually marked center, so the reported percentages are for localization, not boundary segmentation.
  • The 0.9 peak threshold and the overlap step of 4 are fixed in the paper, while the window size $n$ is chosen from the expected ONH diameter, making the method portable across image resolutions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same roundness-of-projection test may transfer to detecting other circular retinal structures, such as the foveal avascular zone, though its contrast behavior differs and would need separate validation.
  • If the method were inverted to look for windows whose projections disagree most, it might act as a detector of non-circular bright lesions such as exudates, a hypothesis the paper does not test.
  • Because the MSE criterion is purely geometric, a straightforward stress test is to synthesize images containing a bright disk that is not the ONH; the paper's claim implies the algorithm would still select it.
  • The failure mode on darker-than-surrounding ONH suggests a practical extension: run the same pipeline on inverted intensities and combine the two scores, which the paper does not address.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The manuscript proposes a method for optic nerve head detection in retinal fundus images, covering both color fundus and fluorescein angiography images. The pipeline consists of: masking the fundus region; partitioning the image into overlapping n×n sub-images; applying the Radon transform to the blue channel of each masked sub-image; selecting candidate windows whose Radon-space peaks exceed a threshold (0.9 of the sub-image maximum); validating candidates by comparing Radon projections across angles via mean-square error; and taking the center of the validated sub-image as the ONH center. Reported results are 100% (40/40 DRIVE), 96.3% (78/81 STARE), 97.5% (117/120 MUMS color), and 91.3% (110/120 MUMS FA), with 4.1 seconds per STARE image. The authors claim the method is computationally more efficient than existing algorithms with comparable detection rates and does not require vessel segmentation.

Significance. If the claims hold, the paper offers a practically useful, lightweight ONH detector: it avoids vessel segmentation, uses public benchmarks (DRIVE, STARE) plus a clinical local database, reports a complexity model in Section III.C, and candidly identifies failure cases such as uneven round-shaped illumination and darker-than-surrounding ONH. The underlying idea of using Radon-projection consistency to capture disk-like bright structures is interesting and the reported processing times are attractive for screening applications. However, the validation step in Section III.B is specified too loosely to be reproduced or to uniquely identify the ONH, and the parameter/evaluation protocol does not rule out in-sample tuning. These issues do not necessarily invalidate the empirical results, but they are load-bearing for the paper's central claim of robust, reproducible detection.

major comments (3)
  1. [Section III.B Step 3, Eq. (4), Figs. 13(h), 14(d), 15(d)] The validation step is not specified sufficiently to support the claimed discrimination or to allow reproduction. Eq. (4) defines an MSE per reference column r within the Radon matrix of one sub-image, but the text never states how candidate sub-images are ranked against each other, how a global final choice is made when several sub-images have comparable MSE values, or what tie-breaking rule is used. This matters because the 'same profile along all directions' property is shared by any centered disk-like bright structure, and every sub-image is multiplied by a circular mask, so the mask alone produces identical projections along all directions. The paper's own failures (Figs. 13(h), 14(d), 15(d)) are attributed to uneven round-shaped illumination, which is precisely the non-uniqueness described. The authors should provide the complete selection rule—including threshold application, cross-sub-image ranking, and tie handling—and ideally add a quantitative experiment showing that bright round exudates and synthetic illumination artifacts do not pass the validation criterion.
  2. [Section III.B Steps 1-3 and Section IV (Table I)] The four free parameters—window size n, overlap step s, Radon peak threshold 0.9, and number of projection angles a—are reported as chosen values, with n set per database, but the manuscript does not state how these values were selected or whether the evaluation datasets were also used for tuning. Since the central claim is robustness across databases and lesion types, the reported detection rates could be optimistic in-sample estimates. Please specify the parameter-selection procedure (e.g., fixed a priori, cross-validated, or tuned on a separate subset), report confidence intervals or bootstrap estimates for the detection rates, and state explicitly which, if any, parameter values were adjusted after looking at the test images.
  3. [Section III.C and Table II] The computational-efficiency claim is not established at the level of rigor used for the accuracy claims. The operation-count formula NM(1+9as^2)+3anc is asserted without a step-by-step derivation, and the runtime comparison in Section IV mixes different hardware generations (Core i5, Centrino, Pentium IV, Core2Duo), different implementations, and different image resolutions. This does not support the headline conclusion that the method is the fastest among methods with comparable detection rates. Please derive the operation count from the actual algorithm steps (windows, Radon calls per window, candidate validation), and, if possible, provide a runtime comparison on identical hardware or a normalized hardware-independent measure.
minor comments (6)
  1. [Section IV] The text refers to '2MHz Intel Core i5', '2MHz Intel Centrino 1.7', and '2MHz Intel Pentium IV'; these should be 2 GHz, and the '2.66 Intel Core2Duo' should read 2.66 GHz.
  2. [Section IV] The sentence reporting 'the average distance (for the 69 successful images) ... and for the 9 successful images' does not reconcile with the earlier statement of 78 correct detections out of 81 STARE images; please clarify the group sizes and what each average refers to.
  3. [Section III.B/III.C] The statement that 'as step is increased, the computation time increases exponentially' is inconsistent with the quadratic dependence NM(1+9as^2); the word should be 'quadratically' unless the formula is corrected.
  4. [Section III.B Step 3 and Section III.C] The symbols M and N are used both for the input image dimensions in Section III.C and for the dimensions of the Radon matrix in Eq. (4); please use distinct notation to avoid confusion.
  5. [Section III.C] The expression '9MNs2a' is missing a superscript; it should be typeset as 9 M N s^2 a for consistency with the total NM(1+9as^2).
  6. [Section III.A.3, Eq. (1)] The integration variable z in Eq. (1) is not explicitly defined; please state the parametrization of the line integral more carefully.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported detection rates are empirical outcomes, not derivations from fitted inputs or self-citations.

full rationale

The paper's central claim is an empirical detection accuracy on DRIVE, STARE, and MUMS-DB images, obtained by a conventional image-processing pipeline (fundus masking, overlapping windows, Radon transform, and an MSE-based validation step). The parameters used (window size n, overlap step 4, and the 0.9 Radon-peak threshold) are explicitly stated as chosen constants; the paper does not claim these are derived from the detection results, nor does it fit them to ground-truth labels in the sense of a regression or classifier whose prediction would reduce to its training targets. Equation (4) defines an MSE similarity between Radon projections and is used to select the roundest bright sub-image; this encodes an assumption about ONH appearance, but it is not a circular definition of the detection target. No load-bearing self-citation or imported uniqueness theorem appears: the cited prior work is used for comparison or background, not to justify the proposed method's validity. The acknowledged failure cases caused by uneven round-shaped illumination and the lack of a held-out tuning protocol are legitimate robustness and generalizability concerns, but they do not make any reported prediction equivalent to an input by construction. The derivation chain is therefore self-contained in the circularity sense.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The method introduces no new physical entities. It rests on hand-set algorithmic parameters and standard image-domain assumptions about optic nerve head appearance. The main burden is that the parameters and assumptions are not independently validated on held-out data.

free parameters (4)
  • Window size n = 313 (MUMS-DB), 79 (DRIVE), 130 (STARE)
    Chosen equal to the maximum optic nerve head diameter in each database. Affects which structures become candidates; no sensitivity analysis is reported.
  • Overlap step s = 4
    Set to 4 for all reported results. Controls the trade-off between computation time and detection robustness; the text says increasing step changes computation time, but no sweep is shown.
  • Radon peak threshold = 0.9 of largest projection in the sub-image
    Candidate sub-images are selected when their peak is above this fraction of the largest projection. The value is chosen for the reported results without validation.
  • Number of Radon projection angles a = 12 (used in complexity formula)
    The complexity expression uses a = 12, but the algorithm description does not specify the projection angle count or how it was chosen.
assumptions (5)
  • domain assumption Radon transform of a bright round object yields a large projection peak along all directions.
    This is the basis for candidate selection and validation in Section III.B, Steps 2 and 3. It is true for an idealized disk but only approximately true for the optic nerve head with its irregular boundary and vessels.
  • domain assumption The blue channel of color fundus images provides sufficient contrast between the optic nerve head and the background.
    Section III.B Step 2 selects the blue component because yellowish patterns have high contrast there. This is presented without quantitative evidence and may fail for unusual illumination or color balance.
  • domain assumption The optic nerve head is brighter than the surrounding retinal pixels.
    Stated in the conclusion as an assumption, and the paper acknowledges failure cases where the optic nerve head is darker. It is load-bearing for the peak detection step.
  • domain assumption Manually marked optic nerve head centers are a reliable ground truth.
    Ground truth in MUMS-DB was marked by a single ophthalmologist. No inter-observer variability is reported, and the 60-pixel success criterion is taken from a prior paper.
  • standard math Standard Radon transform properties and line-integral mathematics are correct.
    The paper relies on standard Radon transform definitions, although the written equations contain sign errors that do not appear to affect the implementation.

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Cite this review

Pith. "Pith review of Computationally Efficient Optic Nerve Head Detection in Retinal Fundus Images." pith.science (2026). https://pith.science/paper/DQQHIEFL

@misc{pith2026190901558,
  author       = {Pith},
  title        = {Pith review of: Computationally Efficient Optic Nerve Head Detection in Retinal Fundus Images},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DQQHIEFL}},
  note         = {Machine review of arXiv:1909.01558}
}
read the original abstract

This paper presents a computationally efficient method for the detection of optic nerve head in both color fundus and fluorescein angiography images. It involves a combination of Radon transformation and multi-overlapping windows within an optimization framework in order to achieve a robust detection in the presence of various structural, color, and intensity variations in such images. Three databases have been examined and it is shown that the introduced method provides high detection rates while achieving faster proceeding rates than the existing algorithms that possess comparable detection rates.

Figures

Figures reproduced from arXiv: 1909.01558 by the authors.

Figure 1
Figure 1. Fundus image from same eye: (a) fluorescein angiography and (b) color fundus image. II. PREVIOUS WORKS There are several existing algorithms that determine the location (generally center) of ONH or its boundary. Sinthanayothin et al. [3] used the area with the highest average intensity variation to detect ONH using an adaptive local contrast enhancement method. Walter and Klein [9] obtained the ONH center as the cen… view at source ↗
Figure 2
Figure 2. Pipeline of the introduced method The main components of the detection algorithm, the ONH detection procedure, and its computational efficiency aspect are covered in the following subsections A, B and C, respectively. A. Algorithm components The algorithm contains three main components as follows. 1. Fundus region detection At first, it is important to separate fundus from background (or region that is out of the fu… view at source ↗
Figure 3
Figure 3. (a) Fundus image, and (b) fundus mask 2. Multi-overlapping window In our introduced method, a fundus image is partitioned into widows or sub-images. To find objects on the border of sub-images, overlapping sliding windows are considered. The size of the targeted object or ONH (n) is used to determine the size of the sub-image or sliding window. A parameter that has influence on the outcome is the windows overlapping… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Window size and overlapping ratio (n, step) in (a) fluorescein angiography, and (b) color fundus image 3. Radon Transform Radon Transform (RT) is widely used in X-ray computed tomography (CT). A projection of a 2D function f(x, y) corresponds to a set of line integrals…
Figure 6
Figure 6. Figure 6: (a) Original fundus image, (b) its blue channel and sub [PITH_FULL_IMAGE:figures/full_fig_p004_6.png]
Figure 7
Figure 7. Figure 7: Masking process in FA: (a) original sub-image, (b) applied mask, (c) masked sub-image [PITH_FULL_IMAGE:figures/full_fig_p004_7.png]
Figure 10
Figure 10. Figure 10: (a) Original fluorescein angiography fundus image from MUMS￾DB, (b) its validated ONH sub-image [PITH_FULL_IMAGE:figures/full_fig_p005_10.png]
Figure 11
Figure 11. Figure 11: (a) Original fundus image from MUMS-DB, (b) its validated ONH sub-image Step 4) ONH detection ONH detection and its changes are quite important in identifying some pathologic conditions. Contrast variance of ONH could generate some artifacts in image segmentation of f…
Figure 14
Figure 14. Figure 14: Outcome of the introduced method applied to sample images of MUMS-DB color database (black cross represents the estimated ONH center): (a)–(c) correct detection, (b) d failure case of the detection [PITH_FULL_IMAGE:figures/full_fig_p006_14.png]
Figure 15
Figure 15. Figure 15: Outcome of the introduced method applied to sample images of MUMS-DB FA database (black cross represents the estimated ONH center): (a)–(c) correct detection, (b) d failure case of the detection C. Computational complexity The main attribute of our algorithm is its co…

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Reviewed August 14, 2026 · model on record in the stance chip above.