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New Real-Time Impulse Noise Removal Method Applied to Chest X-ray Images.
Rashid, Nasr; Berriri, Kamel; Albekairi, Mohammed; Kaaniche, Khaled; Ben Atitallah, Ahmed; Khan, Muhammad Attique; El-Hamrawy, Osama I.
  • Rashid N; Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia.
  • Berriri K; Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Nasr City, Cairo 11884, Egypt.
  • Albekairi M; LAMMDA Laboratory, University of Sousse, Sousse 4054, Tunisia.
  • Kaaniche K; Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia.
  • Ben Atitallah A; Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia.
  • Khan MA; Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia.
  • El-Hamrawy OI; LETI, ENIS, University of Sfax, Sfax 3038, Tunisia.
Diagnostics (Basel) ; 12(11)2022 Nov 09.
Article in English | MEDLINE | ID: covidwho-2109978
ABSTRACT
In this paper, we propose a new Modified Laplacian Vector Median Filter (MLVMF) for real-time denoising complex images corrupted by "salt and pepper" impulsive noise. The method consists of two rounds with three steps each the first round starts with the identification of pixels that may be contaminated by noise using a Modified Laplacian Filter. Then, corrupted pixels pass a neighborhood-based validation test. Finally, the Vector Median Filter is used to replace noisy pixels. The MLVMF uses a 5 × 5 window to observe the intensity variations around each pixel of the image with a rotation step of π/8 while the classic Laplacian filters often use rotation steps of π/2 or π/4. We see better identification of noise-corrupted pixels thanks to this rotation step refinement. Despite this advantage, a high percentage of the impulsive noise may cause two or more corrupted pixels (with the same intensity) to collide, preventing the identification of noise-corrupted pixels. A second round is then necessary using a second set of filters, still based on the Laplacian operator, but allowing focusing only on the collision phenomenon. To validate our method, MLVMF is firstly tested on standard images, with a noise percentage varying from 3% to 30%. Obtained performances in terms of processing time, as well as image restoration quality through the PSNR (Peak Signal to Noise Ratio) and the NCD (Normalized Color Difference) metrics, are compared to the performances of VMF (Vector Median Filter), VMRHF (Vector Median-Rational Hybrid Filter), and MSMF (Modified Switching Median Filter). A second test is performed on several noisy chest x-ray images used in cardiovascular disease diagnosis as well as COVID-19 diagnosis. The proposed method shows a very good quality of restoration on this type of image, particularly when the percentage of noise is high. The MLVMF provides a high PSNR value of 5.5% and a low NCD value of 18.2%. Finally, an optimized Field-Programmable Gate Array (FPGA) design is proposed to implement the proposed method for real-time processing. The proposed hardware implementation allows an execution time equal to 9 ms per 256 × 256 color image.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Year: 2022 Document Type: Article Affiliation country: Diagnostics12112738

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Year: 2022 Document Type: Article Affiliation country: Diagnostics12112738