A statistical edge detection framework for noisy images

Duman, Elvan; Erdem, O. Ayhan

doi:10.1109/siu.2018.8404720

Cited by 2 publications

(5 citation statements)

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“…Optic disk localization was performed by applying the circular Hough transform to the images, which has previously been used in the edge-extraction process. It has been proved in previous studies [25,26] that the performance of the robust rank order-based statistical edge detection method is the most robust to variations in noise and performs better in all noise distributions tested than the conventional edge detection methods. Therefore, compared to the approaches proposed by the researchers given in Table 2, the proposed procedure for OD detection has the advantage that it is applicable to images contaminated with noise.…”

Section: Discussionmentioning

confidence: 93%

“…Next, a modified robust rank order-based edge-detection method was applied [25], which has never been implemented in the OD localization problem and shows better performance than conventional edge-detection methods in noisy images. After obtaining the edges of the image, the circular Hough transform was performed due to the fact that the OD has a circular structure.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Therefore, a statistical edge-detection framework was applied to retinal images, and successful results were obtained in this work. The edge detection performance of the applied method has been proven to be higher in noisy images than other conventional edge-detection methods [25,26].…”

Section: Proposed Methodsmentioning

confidence: 99%

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Statistical Edge Detection and Circular Hough Transform for Optic Disk Localization

et al. 2019

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Accurate and efficient localization of the optic disk (OD) in retinal images is an essential process for the diagnosis of retinal diseases, such as diabetic retinopathy, papilledema, and glaucoma, in automatic retinal analysis systems. This paper presents an effective and robust framework for automatic detection of the OD. The framework begins with the process of elimination of the pixels below the average brightness level of the retinal images. Next, a method based on the modified robust rank order was used for edge detection. Finally, the circular Hough transform (CHT) was performed on the obtained retinal images for OD localization. Three public datasets were used to evaluate the performance of the proposed method. The optic disks were successfully located with the success rates of 100%, 96.92%, and 98.88% for the DRIVE, DIARETDB0, and DIARETDB1 datasets, respectively.

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Section: Discussionmentioning

confidence: 93%

Section: Proposed Methodsmentioning

confidence: 99%

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Statistical Edge Detection and Circular Hough Transform for Optic Disk Localization

et al. 2019

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“…The statistical algorithm RRO offers to detect local changes between neighboring pixels in noisy images via using the edge-height model. In this paper, a Rank Order Test-based edge detector (Lim, 2006;Duman and Erdem, 2018) is used to detect edges of images.…”

Section: Region Growing Segmentationmentioning

confidence: 99%

“…In this paper, an edge detection-based image denoising framework is presented. Firstly modified robust rank order test (MRRO) (Lim, 2006;Duman and Erdem, 2018) is used for edge detection in a noisy image. In this way, all pixels are classified as edge pixels or texture pixels.…”

Section: Introductionmentioning

confidence: 99%

An Edge Preserving Image Denoising Framework Based on Statistical Edge Detection and Bilateral Filter

DUMAN¹

2021

Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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The task of reducing noise from an image is known as image denoising. Although there are various methods and algorithms proposed in the literature, the methods still have limitations. The approaches generally either fail to reduce noise adequately or cause to be lost while effectively reducing noise. Conventional methods have poor performance when considering the success of preserving region boundaries and small structures. Conversely, modern techniques are more effective to smooth images without over smoothing edge details. To address these deficiencies and benefits, in this paper, we aim to develop a framework, which is capable of detecting whether a pixel is a part of edges or textures in an image so the framework can decide which filter should be used depending on region information. The Rank Order Test Method is used to detect image edges. In this way, we determine both which neighbors should be included to build a filter mask in the calculation for each pixel and which filter method should be implemented. We have compared the performance of Bilateral Filter-based methods. Experiments demonstrate that the proposed framework outperforms in terms of both PSNR, SSIM and visual perception for the noise with standard deviations 10, 30, 50. While the average PSNR value was 30.33 DB for the proposed model, the method with the closest result achieved an average score of 28.33 DB.

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A statistical edge detection framework for noisy images

Cited by 2 publications

References 8 publications

Statistical Edge Detection and Circular Hough Transform for Optic Disk Localization

Statistical Edge Detection and Circular Hough Transform for Optic Disk Localization

An Edge Preserving Image Denoising Framework Based on Statistical Edge Detection and Bilateral Filter

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