Medical Image Denoising Using Synergistic Fibroblast Optimization Based Weighted Median Filter

Dhivyaprabha, T T; Jayashree, G. C.; Subashini, P.

doi:10.1109/icaecc.2018.8479516

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…where f (x, y) is the measure of the pixel at coordinates x and y (Dhivyaprabha et al, 2018) and x is the height and y is the width of each block made of g (green), r (red), and b (blue) measures of pixels (Mothkur & Poornima, 2018;Persson & Åstrand, 2008).…”

Section: Dividing the Image Into Regionsmentioning

confidence: 99%

Symptom-Based Identification of G-4 Chili Leaf Diseases Based on Rotation Invariant

2021

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Instinctive detection of infections by carefully inspecting the signs on the plant leaves is an easier and economic way to diagnose different plant leaf diseases. This defines a way in which symptoms of diseased plants are detected utilizing the concept of feature learning (Sulistyo et al., 2020). The physical method of detecting and analyzing diseases takes a lot of time and has chances of making many errors (Sulistyo et al., 2020). So a method has been developed to identify the symptoms by just acquiring the chili plant leaf image. The methodology used involves image database, extracting the region of interest, training and testing images, symptoms/features extraction of the plant image using moments, building of the symptom vector feature dataset, and finding the correlation and similarity between different symptoms of the plant (Sulistyo et al., 2020). This will detect different diseases of the plant.

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Section: Dividing the Image Into Regionsmentioning

confidence: 99%

Symptom-Based Identification of G-4 Chili Leaf Diseases Based on Rotation Invariant

2021

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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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A Novel Gaussian in Denoising Medical Images with Different Wavelets for Internet of Things Devices

Al-Shayea

Mavromoustakis

Batalla

et al. 2020

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

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Medical Image Denoising Using Synergistic Fibroblast Optimization Based Weighted Median Filter

Cited by 3 publications

References 13 publications

Symptom-Based Identification of G-4 Chili Leaf Diseases Based on Rotation Invariant

Symptom-Based Identification of G-4 Chili Leaf Diseases Based on Rotation Invariant

Cervical Precancerous Lesion Image Enhancement Based on Retinex and Histogram Equalization

A Novel Gaussian in Denoising Medical Images with Different Wavelets for Internet of Things Devices

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