Reduction of Gaussian noise from Computed Tomography Images using Optimized Bilateral Filter by Enhanced Grasshopper Algorithm

Bhonsle, Devanand; Bagga, Jaspal; Mishra, Seema; Sahu, Chandrahas; Sahu, Varsha; Mishra, Ashutosh

doi:10.1109/icaect54875.2022.9808017

Cited by 5 publications

(5 citation statements)

References 27 publications

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“…Figure 14 presents the performance analysis of digital filters designed for spatial domain filters for images. In terms of the comparative state-of-the-art, the most popular models are considered, such as the Deep Convolutional Neural Network method (DNCNN) [32], adaptive cuckoo search optimization (AD-CSO) [33], adaptive modified cuckoo search optimization (AD-MCSO) [33], bilateral filter optimization using cuckoo search optimization (BF-CSO) [34], deep image prior combined with regularization by denoising (DeepRED) [35], enhanced GWO optimization [13], unsupervised deep learning (DNN) [36], ant colony optimization (ACO) [37], Bi-dimensional Empirical Mode Decomposition (BEMD) [38], a bilateral filter with a genetic algorithm (BF-GA) [39], the linear minimum mean square error method for principal component analysis (LMMSE-PCA) [40]. These existing state-of-the-art models are compared with the proposed HCPSO on approximate PSNR and SSIM values.…”

Section: Results Analysismentioning

confidence: 99%

“…Sridhar et al [12] proposed an adaptive bilateral filter using the backtracking optimized search algorithm in which the sharpness of edges is considered as the optimization parameter. Bhonsle et al [13] used enhanced grasshopper optimization for denoising medical images. However, this approach was computationally expensive when compared to edge-preserving smoothing.…”

Section: Introductionmentioning

confidence: 99%

“…Sci. 2023,13, 7032 23 of 29 size varied from 128 × 128, 192 × 192, 256 × 256, and 512 × 512. PSNR, SSIM, and FSIM are the maximum for the 512 × 512 image size, i.e., 37.135, 0.940, and 0.906, respectively, for the CHASEDB1 dataset.…”

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confidence: 99%

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A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising

2023

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Medical image denoising is a crucial pre-processing task in the medical field to ensure accurate analysis of anomalies or sicknesses in the human body. Digital filters are popular for reducing undesired noise as they provide reliability, high accuracy, and reduced sensitivity to component tolerances compared to analog filters. However, conventional digital filter design approaches lack efficiency in achieving global optimization robustness. To overcome these incapabilities, this paper adopted bio-inspired optimization algorithms to offer viable digital filter designing tools because of their simple implementation and requirement of a few parameters to control their convergence. This research article explores a hybrid strategy that combines a novel guided decimation box filter (GDBF) with a hybrid cuckoo particle swarm optimization (HCPSO) algorithm to design a denoising filter for medical images. It is the first time a decimation box filter has been used for denoising, leading to novelty. The HCPSO algorithm is applied to obtain the filter parameters optimally. Medical images mostly suffer from four types of noises. The performance of the proposed filter is analyzed for these types of noise. To highlight the importance of parameter selection, the results of the proposed method are compared with other recently utilized bio-inspired genetic algorithms, such as PSO (particle swarm optimization), CS (cuckoo search), and FF (firefly). The superiority (potency) of the proposed method has been established by calculating the improvement in quality parameters such as the peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), and feature similarity index (FSIM). The proposed filter achieved the highest PSNR (~35.7 dB), SSIM (~0.95), and FSIM (~0.92) and proved its numerical and visual quality efficacy over state-of-the-art models.

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Section: Results Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising

2023

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“…Gaussian noise is the type of noise that arises from sensor noise, heat propagation, or circuit noise that affects CT scan images. Gaussian noise introduces random fluctuations in pixel intensity levels across the image, leading to a loss of image clarity, sharpness, and fine details' preservation and restoration [11].…”

Section: Introductionmentioning

confidence: 99%

An Image Denoising Technique Using Wavelet-Anisotropic Gaussian Filter-Based Denoising Convolutional Neural Network for CT Images

Abuya,

Rimiru,

Okeyo

2023

Applied Sciences

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Denoising computed tomography (CT) medical images is crucial in preserving information and restoring images contaminated with noise. Standard filters have extensively been used for noise removal and fine details’ preservation. During the transmission of medical images, noise degrades the visibility of anatomical structures and subtle abnormalities, making it difficult for radiologists to accurately diagnose and interpret medical conditions. In recent studies, an optimum denoising filter using the wavelet threshold and deep-CNN was used to eliminate Gaussian noise in CT images using the image quality index (IQI) and peak signal-to-noise ratio (PSNR). Although the results were better than those with traditional techniques, the performance resulted in a loss of clarity and fine details’ preservation that rendered the CT images unsuitable. To address these challenges, this paper focuses on eliminating noise in CT scan images corrupted with additive Gaussian blur noise (AGBN) using an ensemble approach that integrates anisotropic Gaussian filter (AGF) and wavelet transform with a deep learning denoising convolutional neural network (DnCNN). First, the noisy image is denoised by AGF and Haar wavelet transform as preprocessing operations to eliminate AGBN. The DnCNN is then combined with AGF and wavelet for post-processing operation to eliminate the rest of the noises. Specifically, we used AGF due to its adaptability to edge orientation and directional information, which prevents blurring along edges for non-uniform noise distribution. Denoised images are evaluated using PSNR, mean squared error (MSE), and the structural similarity index measure (SSIM). Results revealed that the average PSNR value of the proposed ensemble approach is 28.28, and the average computational time is 0.01666 s. The implication is that the MSE between the original and reconstructed images is very low, implying that the image is restored correctly. Since the SSIM values are between 0 and 1.0, 1.0 perfectly matches the reconstructed image with the original image. In addition, the SSIM values at 1.0 or near 1.0 implicitly reveal a remarkable structural similarity between the denoised CT image and the original image. Compared to other techniques, the proposed ensemble approach has demonstrated exceptional performance in maintaining the quality of the image and fine details’ preservation.

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“…Furthermore, traditional medical image enhancement algorithms can be divided into spatialdomain-based enhancement methods and frequency-domain-based enhancement methods according to different scopes of image processing [4]. Medical image enhancement methods based on the spatial domain mainly include histogram algorithms [5][6][7][8][9], filter algorithms [10][11][12][13][14], and algorithms based on retinex theory [15][16][17][18][19]. The medical image enhancement algorithm based on the frequency domain converts the image from the spatial domain to the frequency domain and enhances the image with a frequency domain filter.…”

Section: Introductionmentioning

confidence: 99%

Cervical Precancerous Lesion Image Enhancement Based on Retinex and Histogram Equalization

Ren,

Li,

2023

Mathematics

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Cervical cancer is a prevalent chronic malignant tumor in gynecology, necessitating high-quality images of cervical precancerous lesions to enhance detection rates. Addressing the challenges of low contrast, uneven illumination, and indistinct lesion details in such images, this paper proposes an enhancement algorithm based on retinex and histogram equalization. First, the algorithm solves the color deviation problem by modifying the quantization formula of retinex theory. Then, the contrast-limited adaptive histogram equalization algorithm is selectively conducted on blue and green channels to avoid the problem of image visual quality reduction caused by drastic darkening of local dark areas. Next, a multi-scale detail enhancement algorithm is used to further sharpen the details. Finally, the problem of noise amplification and image distortion in the process of enhancement is alleviated by dynamic weighted fusion. The experimental results confirm the effectiveness of the proposed algorithm in optimizing brightness, enhancing contrast, sharpening details, and suppressing noise in cervical precancerous lesion images. The proposed algorithm has shown superior performance compared to other traditional methods based on objective indicators such as peak signal-to-noise ratio, detail-variance–background-variance, gray square mean deviation, contrast improvement index, and enhancement quality index.

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Reduction of Gaussian noise from Computed Tomography Images using Optimized Bilateral Filter by Enhanced Grasshopper Algorithm

Cited by 5 publications

References 27 publications

A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising

A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising

An Image Denoising Technique Using Wavelet-Anisotropic Gaussian Filter-Based Denoising Convolutional Neural Network for CT Images

Cervical Precancerous Lesion Image Enhancement Based on Retinex and Histogram Equalization

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