Effect of trigonometric functions-based watermarking on blood vessel extraction: an application in ophthalmology imaging

Dey, Nilanjan; Ahmed, Sk. Saddam; Chakraborty, Sayan; Maji, Prasenjit; Das, Achintya; Chaudhuri, Sheli Sinha

doi:10.1504/ijes.2017.081720

Cited by 4 publications

(5 citation statements)

References 24 publications

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“…In the previous works regarding the SGO algorithm, a value of 0.2 for the parameter c [29,30] was suggested, but, here, a trial and error procedure was performed and the value that offered the best convergence rate along with the optimized result was found to be 0.5. The experimental study also confirmed that the proposed SGO algorithm offered better execution time compared to other well-known heuristic algorithms such as PSO [52], FA [42,47] and BA [41] based thresholding procedures. Examples of the used RGB images are presented in Figure 3 along with the corresponding histogram of the three channels (i.e., R, G and B components) and the associated ground truth.…”

Section: Resultssupporting

confidence: 66%

“…where R a and R b are random numbers having the range [0,1] and X r,j is the knowledge (position) value of the chosen individual. From Equations (2) and 3, it can be observed that the implementation of the SGO algorithm is simple compared to other algorithms existing in the image processing domain [40][41][42][43][44][45][46][47][48]. The steps of the standard SGO algorithm can be described as in Algorithm 1.…”

Section: Social Group Optimizationmentioning

confidence: 99%

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Social Group Optimization Supported Segmentation and Evaluation of Skin Melanoma Images

et al. 2018

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The segmentation of medical images by computational methods has been claimed by the medical community, which has promoted the development of several algorithms regarding different tissues, organs and imaging modalities. Nowadays, skin melanoma is one of the most common serious malignancies in the human community. Consequently, automated and robust approaches have become an emerging need for accurate and fast clinical detection and diagnosis of skin cancer. Digital dermatoscopy is a clinically accepted device to register and to investigate suspicious regions in the skin. During the skin melanoma examination, mining the suspicious regions from dermoscopy images is generally demanded in order to make a clear diagnosis about skin diseases, mainly based on features of the region under analysis like border symmetry and regularity. Predominantly, the successful estimation of the skin cancer depends on the used computational techniques of image segmentation and analysis. In the current work, a social group optimization (SGO) supported automated tool was developed to examine skin melanoma in dermoscopy images. The proposed tool has two main steps, mainly the image pre-processing step using the Otsu/Kapur based thresholding technique and the image post-processing step using the level set/active contour based segmentation technique. The experimental work was conducted using three well-known dermoscopy image datasets. Similarity metrics were used to evaluate the clinical significance of the proposed tool such as Jaccard's coefficient, Dice's coefficient, false positive/negative rate, accuracy, sensitivity and specificity. The experimental findings suggest that the proposed tool achieved superior performance relatively to the ground truth images provided by a skin cancer physician. Generally, the proposed SGO based Kapur's thresholding technique combined with the level set based segmentation technique is very effective for identifying melanoma dermoscopy digital images with high sensitivity, specificity and accuracy.

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Section: Resultssupporting

confidence: 66%

Section: Social Group Optimizationmentioning

confidence: 99%

Social Group Optimization Supported Segmentation and Evaluation of Skin Melanoma Images

et al. 2018

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“…The authors indicate that they used the green channel of the original image to generate the watermark, and the other channels (red and blue) were used to embed the watermark. In [12], Dey et al presented a watermarking method applied to fundus images to insert an EPR into the blood vessels extracted using K-means segmentation. Additionally, the authors evaluated and found a percentage difference in accuracy (0.25%) in blood vessel extraction before and after watermarking.…”

Section: Watermarking Algorithmsmentioning

confidence: 99%

“…[9] presents an imperceptible watermark method for medical images and the authors include tests demonstrating that it does not affect computer vision-based automated diagnosis of retinal diseases. On the other hand, Dey et al [12] reported a watermarking algorithm for fundus images by inserting patient information, and evaluated that the embedding watermark process slightly modifies the blood vessel extraction.…”

mentioning

confidence: 99%

“…The selected coefficient allows for inserting the watermark into the blood vessel patterns, giving a natural and automatic ROI-based watermarking method. Thus, the proposed method is an alternative to Dey et al [12], where the blood vessels are first extracted using K-means segmentation and then the EPR is hidden into them using interpolation and trigonometric functions. Also, our approach differs from the work of Singh et al [15], where a unique identification code is inserted into the blood vessels detected in fundus images.…”

mentioning

confidence: 99%

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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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Robotics in Healthcare: An Internet of Medical Robotic Things (IoMRT) Perspective

Guntur

Gorrepati

Dirisala

2019

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging

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Effect of trigonometric functions-based watermarking on blood vessel extraction: an application in ophthalmology imaging

Cited by 4 publications

References 24 publications

Social Group Optimization Supported Segmentation and Evaluation of Skin Melanoma Images

Social Group Optimization Supported Segmentation and Evaluation of Skin Melanoma Images

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

Robotics in Healthcare: An Internet of Medical Robotic Things (IoMRT) Perspective

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