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REVIEW 5 major objections 5 minor 36 references

A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy

T0 review · 5 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Scanning overlapping windows in fluorescein angiography with the Radon transform can flag diabetic retinopathy by detecting microaneurysms, with 94–100% sensitivity and 70–75% specificity.

desk verdict Solid classical FA microaneurysm pipeline, but headline specificity is statistically fragile and needs a consistency correction. read the letter →

arxiv 1909.01557 v1 pith:BRNF6K6K submitted 2019-09-04 physics.med-ph eess.IV

Meysam Tavakoli , Reza Pourreza Shahri , Hamidreza Pourreza , Alireza Mehdizadeh , Touka Banaee , Mohammad Hosein Bahreini Toosi This is my paper · ORCID
classification physics.med-pheess.IV MSC 92C5568U10
keywords diabeticretinopathymicroaneurysmdetectionfluoresceinangiographyRadontransformfundusimageanalysiscomputeraideddiagnosissegmentationscreening
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 sets out to show that microaneurysms — the earliest visible retinal sign of diabetic retinopathy — can be detected automatically in fluorescein angiography images by applying the Radon transform to small overlapping windows and looking for circular patterns that give the same peak profile at every projection angle. The authors argue this is a useful complement to morphology-based detection because the same transform also supplies numerical data such as position, size, and orientation. In testing, image-level diagnosis of diabetic retinopathy reached a sensitivity and specificity of 94% and 75% on a 120-image local database and 100% and 70% on a 50-image second database, with lesion-level sensitivities of 92–95% on these two sets. The clinical motivation is that automated, deterministic pre-screening could catch the first sign of diabetic retinopathy while cutting the number of normal images a specialist has to read.

What carries the argument

The Radon transform, defined as the integral of image intensity over straight lines, $\\int f(x,y)\\,\\delta(x\\cos\\theta+y\\sin\\theta-s)\\,dx\\,dy$, is the central object. Applied to each small overlapping sub-image, a round microaneurysm produces a peak whose profile is nearly identical in every projection angle, while elongated vessels produce peaks that vary strongly with angle; this contrast is the detection criterion. The multi-overlapping window strategy ensures lesions near sub-image borders are not missed. The same transform is reused earlier to find the optic nerve head (by roundness) and to segment the vascular tree (by line peaks), and those masks remove the two major sources of false positives before microaneurysm detection begins.

What would settle it

Take a new set of fluorescein angiography images with expert-annotated microaneurysm locations, implement the pipeline with the paper's stated window sizes, step values, and threshold constants, and compare detected candidates against the annotations; the central claim is weakened if many true microaneurysms located next to vessels are masked as vessels, or if laser scars and abnormal dilated capillaries are counted as microaneurysms at the reported false-positive rate.

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Extended reading notes

Core claim

The discovery is that a microaneurysm can be recognised in the Radon domain as a peak that appears in every projection column with nearly Gaussian, mutually similar profiles, because a round object yields essentially identical projections at all angles. After masking the optic nerve head and the vascular tree — both found with the same Radon-transform machinery on overlapping sub-images — the authors threshold remaining candidates by intensity, size under 125 μm, and roundness of the Radon profiles. On image-level detection of diabetic retinopathy they report sensitivity and specificity of 94% and 75% for the first local database and 100% and 70% for the second; on lesion-level detection the sensitivity is 92%, 95%, and 91% across the two local databases and the public challenge subset, indicating the method can also support microaneurysm counting for follow-up. The paper presents the system as a complementary diagnostic aid rather than a full screening package, and notes that adding image registration would make it useful for tracking disease progression.

Load-bearing premise

The method relies on the assumption that a true microaneurysm looks round at every angle in the Radon domain while vessel endings, branch points, noise, and laser scars do not, and that the empirically chosen window sizes, step values, and thresholds keep this separation working on new image collections.

Editorial extensions

If this is right

  • Automated fluorescein-angiography reading could flag the earliest stage of diabetic retinopathy with image-level sensitivity of 94–100%, meaning few diseased eyes would be sent back as healthy.
  • A deterministic pipeline of this kind gives repeatable readings that do not degrade with reader fatigue, so it could sit in front of a human specialist and separate clearly normal images.
  • The paper's ablation shows the system depends on vessel masking: without it, specificity drops from 75% to 61% on the first local database and from 70% to 56% on the second, so improvements in vessel segmentation should translate directly into better microaneurysm screening.
  • With an added image-registration step, the same microaneurysm detection could support longitudinal counting of microaneurysms for treatment follow-up.

Reading between the lines

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

  • If the Radon-profile consistency criterion transfers to other imaging hardware, the same circular-spot detector could be retrained for other round retinal lesions, such as hard exudates or drusen in color fundus photography; the paper only tests fluorescein angiography images.
  • The roughly 11-minute per-image runtime suggests that the pipeline, as published, is a diagnostic aid rather than a population-level screening tool; deployment would require optimising the vessel-detection step, which dominates the runtime.
  • Because specificity (70–75%) trails sensitivity (94–100%), an automated triage built on this method would still refer a substantial fraction of normal patients to human readers; the acceptable trade-off would depend on the cost of false alarms versus missed disease.
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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

5 major / 5 minor

Summary. The paper proposes an automated pipeline for detecting microaneurysms (MAs) in fluorescein angiography (FA) fundus images, using a Radon transform combined with multi-overlapping windows. The pipeline first detects and masks the optic nerve head and the vascular tree, then identifies MAs as circular bright patterns whose Radon profiles are consistent across projection angles. The authors evaluate the method on two local databases (MUMS-DB and Second Local Database) and a subset of the public ROC database, reporting image-level DR detection with sensitivities/specificities of 94%/75% and 100%/70% for the two local databases, and lesion-level sensitivities of 92%, 95%, and 91% for MUMS, 2nd-DB, and ROC, respectively. The paper also compares results with human readers and prior methods, and reports processing times.

Significance. If the reported performance holds under independent validation, the work would demonstrate a useful complementary approach for MA detection in FA images, a clinically important task for diabetic retinopathy screening. The use of FA as a gold-standard modality, the inclusion of three databases with different cameras and resolutions, and the reporting of both image-level and lesion-level analyses are strengths. The Radon-transform-based detection of circular patterns combined with vessel/ONH masking is a reasonable and somewhat distinctive idea. However, the significance is currently limited by the small number of normal images used for specificity estimation, the per-database empirical tuning of many parameters, and unresolved numeric inconsistencies in the reported results.

major comments (5)
  1. [Section 4.4.2 and Table 6] The ROC database lesion-level results are internally inconsistent. The text states that among 135 MAs the method found 122 true positives and missed 13, yielding a sensitivity of 91%; Table 6 reports 125 true positives and 10 false negatives. These two sets of numbers cannot both be correct, and the reported sensitivity is consistent only with the 122/135 values. Additionally, the text says the false-positive count is 41 and the FP per image is 5, but 41 divided by 22 (or even 20) is not 5. These inconsistencies must be resolved because the lesion-level results are a central part of the evaluation.
  2. [Section 4.4.1, Tables 2 and 3] The image-level specificity estimates are based on very small numbers of normal images: 20 in MUMS-DB and 10 in 2nd-DB. With 15/20 true negatives the exact binomial 95% confidence interval is roughly 53-89%, and with 7/10 it is roughly 39-90%; the latter interval includes 50%, so the reported 70% specificity is not statistically distinguishable from chance at the 95% level. The paper should either provide confidence intervals for all reported sensitivities and specificities or enlarge the normal-image sample before claiming specific performance levels.
  3. [Sections 3.3, 3.5.1, 3.5.2 and 4.2] Many key parameters are chosen empirically and per database: window sizes for ONH, vessel, and MA detection (n=309/38/114, n=30/15/17, n=18/10/12), sliding steps (4 or 5), the vessel width parameter alpha (0.75 or 0.6), top-hat disk diameter (d=25), averaging filter size (75), and the DR diagnosis threshold. Several of these are set using object-size statistics (e.g., 'maximum diameter of the biggest MA in pixel') that are computed from each database, including the test set. This means the reported performance is not an independent test of a fixed algorithm but reflects database-specific tuning. The authors should validate with cross-validation within each database or with parameters fixed across databases, and report how performance varies with parameter choices.
  4. [Sections 4.4.1 and 4.4.2] The average number of false positives per image is reported as 5 for both local databases, which is exactly the threshold used for image-level DR diagnosis ('more than 5 MAs' implies DR). This places the image-level classification very close to the operating point expected for a normal image, so the reported specificity of 75%/70% is fragile: any increase in the FP rate would rapidly convert normal images into false positives. The paper should report the distribution of FP counts across images and show how the diagnosis threshold is positioned with respect to that distribution.
  5. [Section 3.5.2] The MA validation criterion is described qualitatively: profiles related to projections have 'minimum deviation' with each other, and a candidate is accepted if its Radon peaks are 'approximately similar' across projection angles. No explicit metric or threshold is defined for this deviation, and the intensity threshold is also only described as 'predefined.' This lack of a precise decision rule makes the method irreproducible and prevents independent verification of the reported lesion-level accuracy. The authors should state the exact formula and threshold used to accept or reject a candidate MA.
minor comments (5)
  1. [Section 3.2] The equations defining the Radon transform appear to be missing from the manuscript; only the equation numbers are visible. This needs to be fixed so that the mathematical basis is complete.
  2. [Section 4.2] The number of ROC images is inconsistent: the introduction and Section 3 state 22 images, while Section 4.2 says the test set included 20 images from the ROC database. Please reconcile these counts.
  3. [Section 3.5.1] In the third paragraph, the text says 'the optimum of step was selected 5 pixels for both databases empirically,' but the method is applied to three databases. Please clarify which databases are meant.
  4. [Sections 4.1 and 4.4.2] The paper alternates between 'pixel based' and 'lesion based' analysis; the latter term is used for the actual evaluation. Please define the terminology consistently, since lesion-based analysis is not the same as pixel-based analysis.
  5. [Section 4.5] The comparison with previous work would be stronger if the evaluation protocol (e.g., whether the same images are used for training and testing, and how the 5-FP-per-image threshold was chosen) were explicitly matched across studies. Currently, the comparison with Cree et al. and Spencer et al. is qualitative and does not account for differences in image sets and ground-truth definitions.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported sensitivities/specificities are measured on held-out test images against expert ground truth, and the self-citations are to separately published component methods.

full rationale

The central claim is an empirical evaluation of an MA-detection pipeline. The paper separates a training set (45 images) from a test set (145 images) and states: 'After fixing the parameters of our algorithm by using training set, automated method was tested in each image of our three databases' (Section 4.2). The reported image-level and lesion-level sensitivities are counts of true positives against ophthalmologist-marked ground truth, not algebraic consequences of the fitted window sizes, step sizes, alpha values, or thresholds; those parameters were tuned on the training set and then applied to unseen test images. No equation in the paper defines the output in terms of the fitted parameters in a way that would force the reported accuracy. The self-citations to [30], [31], [35], and [36] describe ONH detection and vessel segmentation components; these are auxiliary steps, described in the text and previously published, and the final evaluation is against external expert labels and an external database (ROC), so the citations are not the sole support for the central claim. The small numbers of normal images underlying the specificity estimates (20 and 10) and the empirically chosen thresholds are validity concerns, but they are not circularity: a small or fragile test set is not an input-output identity. Therefore no circular step can be identified from the paper's own equations or construction.

Assumptions & free parameters 11 free parameters · 4 assumptions · 0 invented entities

The central claim rests on a large number of hand-tuned parameters and several domain assumptions about the appearance of microaneurysms and the reliability of expert ground truth. No new physical entities are introduced.

free parameters (11)
  • ONH window size n = MUMS: 309 px, 2nd-DB: 38 px, ROC: 114 px
    Chosen as maximum diameter of the largest optic nerve head in each database (Section 3.3).
  • ONH sliding step = 4 pixels for all databases
    Empirically selected as optimum, trading off speed and detection (Section 3.3).
  • Vessel detection window size = MUMS: 30 px, 2nd-DB: 15 px, ROC: 17 px
    Selected based on width of the largest vessel in each database (Section 3.5.1).
  • Vessel detection step = 5 pixels
    Empirically selected for all databases (Section 3.5.1).
  • Vessel width parameter alpha = 0.75 for MUMS, 0.6 for 2nd-DB and ROC
    Constant between 0 and 1 used to determine vessel thickness from the Radon profile (equations 5 and 6).
  • MA detection window size = MUMS: 18 px, 2nd-DB: 10 px, ROC: 12 px
    Chosen as maximum diameter of the largest microaneurysm in pixels, found empirically (Section 3.5.2).
  • MA detection step = 5 pixels
    Empirically selected optimum (Section 3.5.2).
  • Top-hat disk diameter = 25 pixels
    Empirically found to be the best compromise for segmentation between features and background (Section 3.4).
  • Averaging filter window size = 75 pixels
    Used to remove point noise; size stated without derivation (Section 3.4).
  • DR diagnosis threshold = more than 5 MAs per image
    Clinical decision rule for image-level diagnosis (Section 4.1).
  • Radon peak validation threshold = not specified numerically
    'Predefined threshold' referenced for candidate validation but not quantified (Sections 3.3 and 3.5.2).
assumptions (4)
  • standard math Radon transform of a circle produces similar profiles across all projection angles.
    Invoked in Sections 3.3 and 3.5.2 to detect the optic nerve head and microaneurysms.
  • domain assumption Microaneurysms appear as bright, roughly circular spots with diameter less than 125 micrometers.
    Used as the detection criterion in Section 3.5.2; not independently validated in this paper.
  • domain assumption Fluorescein angiography is an appropriate gold standard for microaneurysm detection.
    Assumed in Section 2 and used to justify the ground truth.
  • ad hoc to paper The empirically chosen parameters and thresholds generalize from the training set to the test populations.
    The method depends on parameters selected from a small, biased training set (Section 4.2); no external validation of parameter transfer is provided.

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Cite this review

Pith. "Pith review of A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy." pith.science (2026). https://pith.science/paper/BRNF6K6K

@misc{pith2026190901557,
  author       = {Pith},
  title        = {Pith review of: A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BRNF6K6K}},
  note         = {Machine review of arXiv:1909.01557}
}
read the original abstract

Early detection of microaneurysms (MAs), the first sign of Diabetic Retinopathy (DR), is an essential first step in automated detection of DR to prevent vision loss and blindness. This study presents a novel and different algorithm for automatic detection of MAs in fluorescein angiography (FA) fundus images, based on Radon transform (RT) and multi-overlapping windows. This project addresses a novel method, in detection of retinal land marks and lesions to diagnose the DR. At the first step, optic nerve head (ONH) was detected and masked. In preprocessing stage, top-hat transformation and averaging filter were applied to remove the background. In main processing section, firstly, we divided the whole preprocessed image into sub-images and then segmented and masked the vascular tree by applying RT in each sub-image. After detecting and masking retinal vessels and ONH, MAs were detected and numbered by using RT and appropriated thresholding. The results of the proposed method were evaluated reported on three different retinal images databases, the Mashhad Database with 120 FA fundus images, Second Local Database from Tehran with 50 FA retinal images and a part of Retinopathy Online Challenge (ROC) database with 22 images. Automated DR detection demonstrated a sensitivity and specificity of 94% and 75% for Mashhad database and 100% and 70% for the Second Local Database respectively.

Figures

Figures reproduced from arXiv: 1909.01557 by the authors.

Figure 1
Figure 1. Block diagram of proposed method 3.1. Multi-overlapping window In the proposed algorithm fundus image was partitioned into widows or sub-images. To determine the size of each sub-image, we used our knowledge database. In this regard, size [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 3
Figure 3. Radon transform. Equation (1) can be expressed in a single integral as: Where In which the z-axis, in [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Parallel projection line integral RT is integral of an image over straight lines in specified angle. It makes our algorithm sturdy and less sensitive to noise than other algorithms because intensity variations due to noise tend to be omitted by process of integration. In spite of morphologic operators which are image based and could not provide numerical data directly, by using RT object finding and description will… view at source ↗
Figures from the paper (2 more)
Figure 6
Figure 6. Figure 6: (a) FA fundus image (b) top-hat result and contrast stretching (c) result of subtraction of top-hat and filtered top-hat image. 3.5. Main processing The results of preprocessing stage were utilized as input images for main processing. After preprocessing, vascular netw…
Figure 7
Figure 7. Figure 7 [PITH_FULL_IMAGE:figures/full_fig_p018_7.png]

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Reference graph

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