Classification of Fundus Images Based on Non-binary Patterns for the Automated Screening of Retinal Lesions

Suresh, Mekhana; Indira, Sreelekshmi; Ramachandran, Sivakumar

doi:10.1007/978-981-16-1089-9_60

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…Previous studies of AMD recognition focused on recognising AMD from normal images 4,9–11,30–35 . However, in routine clinical practice, other co‐existing ocular diseases, and conditions with similar phenotypic characteristics to AMD may present, making the diagnosis of AMD challenging.…”

Section: Methodsmentioning

confidence: 99%

“…Previous studies of AMD recognition focused on recognising AMD from normal images. 4 , 9 , 10 , 11 , 30 , 31 , 32 , 33 , 34 , 35 However, in routine clinical practice, other co‐existing ocular diseases, and conditions with similar phenotypic characteristics to AMD may present, making the diagnosis of AMD challenging. Therefore, we developed various categories covering AMD and a range of ocular diseases that could confound AMD versus non‐AMD classification.…”

Section: Methodsmentioning

confidence: 99%

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Drusen‐aware model for age‐related macular degeneration recognition

Pan

et al. 2023

Ophthalmic Physiologic Optic

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Introduction The purpose of this study was to build an automated age‐related macular degeneration (AMD) colour fundus photography (CFP) recognition method that incorporates confounders (other ocular diseases) and normal age‐related changes by using drusen masks for spatial feature supervision. Methods A range of clinical sources were used to acquire 7588 CFPs. Contrast limited adaptive histogram equalisation was used for pre‐processing. ResNet50 was used as the backbone network, and a spatial attention block was added to integrate prior knowledge of drusen features into the backbone. The evaluation metrics used were sensitivity, specificity and F1 score, which is the harmonic mean of precision and recall (sensitivity) and area under the receiver‐operating characteristic (AUC). Fivefold cross‐validation was performed, and the results compared with four other methods. Results Excellent discrimination results were obtained with the algorithm. On the public dataset (n = 6565), the proposed method achieved a mean (SD) sensitivity of 0.54 (0.09), specificity of 0.99 (0.00), F1 score of 0.62 (0.06) and AUC of 0.92 (0.02). On the private dataset (n = 1023), the proposed method achieved a sensitivity of 0.92 (0.02), specificity of 0.98 (0.01), F1 score of 0.92 (0.01) and AUC of 0.98 (0.01). Conclusion The proposed drusen‐aware model outperformed baseline and other vessel feature‐based methods in F1 and AUC on the AMD/normal CFP classification task and achieved comparable results on datasets that included other diseases that often confound classification. The method also improved results when a five‐category grading protocol was used, thereby reflecting discriminative ability of the algorithm within a real‐life clinical setting.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Drusen‐aware model for age‐related macular degeneration recognition

Pan

et al. 2023

Ophthalmic Physiologic Optic

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“…The model was trained on the APTOS dataset and was tested using the APTOS, MESSIDOR, and IDRiD datasets. Suresh et al [34] presented a screening technique that relies on the texture analysis of the retinal background using Local Ternary Patterns (LTP). Also, it compared the results obtained using the proposed approach with Local Binary Patterns (LBP) instead of LTP.…”

Section: Deep Learning Classification Algorithmsmentioning

confidence: 99%

Bio-Inspired Watermarking Method for Authentication of Fundus Images in Computer-Aided Diagnosis of Retinopathy

Moya-Albor,

Gomez-Coronel,

Brieva

et al. 2024

Mathematics

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Nowadays, medical imaging has become an indispensable tool for the diagnosis of some pathologies and as a health prevention instrument. In addition, medical images are transmitted over all types of computer networks, many of them insecure or susceptible to intervention, making sensitive patient information vulnerable. Thus, image watermarking is a popular approach to embed copyright protection, Electronic Patient Information (EPR), institution information, or other digital image into medical images. However, in the medical field, the watermark must preserve the quality of the image for diagnosis purposes. In addition, the inserted watermark must be robust both to intentional and unintentional attacks, which try to delete or weaken it. This work presents a bio-inspired watermarking algorithm applied to retinal fundus images used in computer-aided retinopathy diagnosis. The proposed system uses the Steered Hermite Transform (SHT), an image model inspired by the Human Vision System (HVS), as a spread spectrum watermarking technique, by leveraging its bio-inspired nature to give imperceptibility to the watermark. In addition, the Singular Value Decomposition (SVD) is used to incorporate the robustness of the watermark against attacks. Moreover, the watermark is embedded into the RGB fundus images through the blood vessel patterns extracted by the SHT and using the luma band of Y’CbCr color model. Also, the watermark was encrypted using the Jigsaw Transform (JST) to incorporate an extra level of security. The proposed approach was tested using the image public dataset MESSIDOR-2, which contains 1748 8-bit color images of different sizes and presenting different Diabetic Retinopathy (DR). Thus, on the one hand, in the experiments we evaluate the proposed bio-inspired watermarking method over the entire MESSIDOR-2 dataset, showing that the embedding process does not affect the quality of the fundus images and the extracted watermark, by obtaining average Peak Signal-to-Noise Ratio (PSNR) values higher to 53 dB for the watermarked images and average PSNR values higher to 32 dB to the extracted watermark for the entire dataset. Also, we tested the method against image processing and geometric attacks successfully extracting the watermarking. A comparison of the proposed method against state-of-the-art was performed, obtaining competitive results. On the other hand, we classified the DR grade of the fundus image dataset using four trained deep learning models (VGG16, ResNet50, InceptionV3, and YOLOv8) to evaluate the inference results using the originals and marked images. Thus, the results show that DR grading remains both in the non-marked and marked images.

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Classification of Fundus Images Based on Non-binary Patterns for the Automated Screening of Retinal Lesions

Cited by 2 publications

References 16 publications

Drusen‐aware model for age‐related macular degeneration recognition

Drusen‐aware model for age‐related macular degeneration recognition

Bio-Inspired Watermarking Method for Authentication of Fundus Images in Computer-Aided Diagnosis of Retinopathy

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