A Robust Denoising Filter with Adaptive Edge Preservation

Chiu, Li-Cheng; Fuh, Chiou-Shann

doi:10.1007/978-3-540-89796-5_115

Cited by 2 publications

(2 citation statements)

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“…They were able to reduce the phantom colour that appears in the original image. Numerous of researchers applied bilateral filter to obtain smooth images while preserving important details [6][7][8][9] . Global threshold was applied to detect border of the skin lesion and refine the border using the optimal channels from CIEXYZ colour space [11] .…”

Section: Related Workmentioning

confidence: 99%

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Skin Lesion Border Detection Based on Best Statistical Model Using Optimal Colour Channel

Al-abayechi¹,

Abu-Almash²

2020

J Autonom Intell

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This paper proposes an effective way to segment melanoma skin lesion in colour dermoscopic images, using an edge-based approach. The proposed method, different methods were combined to improve the segmentation performance. These methods are morphological operations, bilateral filter, spline, polynomial model and canny edge detector. Different methods were tested to select the best method that was produced the best outcome. These testing methods, bilateral filter provided the highest PSNR amongst other filters such as median filter, Gaussian and average filter. Two statistical models were implemented polynomial model and linear regression and selected the best performance as polynomial model. Four edge detectors were applied to detect the edge of skin lesion and select the best segmentation accuracy. Manual border selection was used as the benchmark to evaluation the accuracy of the automatic border. The proposed method was able to achieve a good average accuracy of 96.69% based on canny edge detector. Our dataset consists of (70) dermoscopic images that includes melanoma and nevus.

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Section: Related Workmentioning

confidence: 99%

“…where x and y represent the spatial coordinates of the pixel, and D is the spatial scale. The range filter weights pixels based on tonal difference given by the following function: was computed before and after adding noise by the following equations [6,9] :…”

Section: Rescaling Image Using Bilt-spmentioning

confidence: 99%