Contrast Enhancement of an Image using Fuzzy Logic

Sharma, Sonal; Bhatia, Avani

doi:10.5120/19757-1410

Cited by 11 publications

(5 citation statements)

References 11 publications

(14 reference statements)

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“…21 Although fuzzy theory may result in more computational overhead, it permits flexible reasoning. 22 While neural networks, particularly Convolutional Neural Networks (CNNs), yield excellent-quality results, they are not easily interpretable and require much data and computational power. 23,24 The investigation of recent image enhancement techniques in breast cancer diagnosis has exhibited commendable advancements, yet they are not without limitations.…”

Section: Introductionmentioning

confidence: 99%

“…While improving colour constancy, the Retinex model has drawbacks in complex lighting conditions and can be computationally expensive 21 . Although fuzzy theory may result in more computational overhead, it permits flexible reasoning 22 . While neural networks, particularly Convolutional Neural Networks (CNNs), yield excellent‐quality results, they are not easily interpretable and require much data and computational power 23,24 …”

Section: Introductionmentioning

confidence: 99%

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Breast cancer diagnosis through an optimization‐driven multispectral gamma correction (ODMGC)

Ahmad,

2024

Adaptive Control & Signal

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SummaryThe Optimization‐Driven Multispectral Gamma Correction (ODMGC) algorithm overcomes challenges in gathering subtle information and detecting cancer in dense breast thermograms. This algorithm enhances the accuracy of true positives and true negatives while minimising false negatives and false positives. The ODMGC involves a multi‐step optimisation process that categorises grey‐scale images of breast thermograms based on mean brightness. Then, based on the grey levels of the pixels, we grouped each categorisation into sub‐regions. Followed by each group has undergone individually optimised base enhancement. This process enhances the contrast between cancerous and normal tissues, eliminates over‐ and under‐enhancement, and supports breast tumour diagnosis. The optimised‐based enhancement images serve as a reference point for the histogram specification of the V component of the thermograms in the HSV (Hue, Saturation, and Value) model. Further, we evaluated the proposed model using both qualitative and quantitative measures. Finally, using dimension‐reduced significant Grey‐Level Co‐occurrence Matrix (GLCM) features, we validated the results with a Random Forest (RF) classifier. The algorithm was successfully implemented in MATLAB 2020a, and the classifier was developed in Jupyter Notebook using Python. The subjective comparison confirmed the proposed method's superior resolution in normal and malignant cases. The classifier results showed an accuracy of 96.4%, sensitivity of 98.1%, and specificity of 96.9%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Breast cancer diagnosis through an optimization‐driven multispectral gamma correction (ODMGC)

Ahmad,

2024

Adaptive Control & Signal

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“…Furthermore, current methods for image en-hancement are based on transformational domain methods that may introduce color artifacts and reduce the intensity of input remote sensing images. To overcome this problem, Sharma et al [21] introduced a modified approach, which has the potential to effectively enhance the contrast in digital images using a modified fuzzy-based development algorithm.…”

Section: Introductionmentioning

confidence: 99%

Low contrast enhancement technique for color images using interval-valued intuitionistic fuzzy sets with contrast limited adaptive histogram equalization

Jebadass

Balasubramaniam

2022

Soft Comput

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This work introduces a program to enhance images taken in low light. Fuzzy set theory is creating a significant shift in image processing. Interval-valued intuitionistic fuzzy sets (IVIFS) based on intuitionistic fuzzy sets constructed from fuzzy sets are used to enhance images taken in low light. In the proposed method, first the given low light image is fuzzified by normal fuzzification. Then the fuzzified image is converted to an interval-valued intuitionistic fuzzy image. This image will be proposed enhanced image after applying the contrast limited adaptive histogram equalization (CLAHE). The experimental results reveal that the proposed method gives better results when compared with other existing methods like histogram equalization (HE), CLAHE, brightness preserving dynamic fuzzy histogram equalization (BPDFHE), histogram specification approach (HSA). Based on the performance analysis like entropy and correlation coefficient (CC), the proposed method gives better results.

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“…A technique based on fuzzy sets can provide a framework for incorporating human knowledge in the solution of problems with a formulation based on imprecise concepts (Gonzalez, Woods, & Eddins, 2009). The studies of Kaur and Sidhu (2015), Gupta, Chauhan, and Shrivastava (2016), and Sharma and Bhatia (2015) presented the application of fuzzy set theory in image enhancement.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Firefly and Cuckoo Search Algorithms in Optimal Thresholding of Cancer Cell Images

Wonohadidjojo

2019

ComTech

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This research presented a performance comparison of the two methods in cancer cells image processing. Each method consisted of two stages. The first stage was image enhancement using fuzzy sets. The second stage was optimal fuzzy entropy based image thresholding. In the thresholding stage, the first method used Firefly Algorithm (FA) and the second used Cuckoo Search (CS). In both methods, four performance metrics (Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structured Similarity Indexing Method (SSIM), and Feature Similarity Indexing Method (FSIM)) and variance and entropy of the images were computed to validate the comparison. The image histograms of both methods show that the distribution of red, green, and blue channel is better than the histograms of original images. In terms of the four metrics, the method that uses FA shows higher performance than CS. In terms of image variance and entropy, the method using CS shows better results than FA. These results suggest that when the performance metrics used are MSE, PSNR, MSSIM, and FSIM, the method using FA is more suitable for cancer cells image enhancement and thresholding. However, when the variance and entropy of the images are used as the performance metrics, the method using CS is more suitable for cancer cells image enhancement and thresholding. Both methods will be useful to assist in the analysis of cancer cell images by the experts in the field.

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Contrast Enhancement of an Image using Fuzzy Logic

Cited by 11 publications

References 11 publications

Breast cancer diagnosis through an optimization‐driven multispectral gamma correction (ODMGC)

Breast cancer diagnosis through an optimization‐driven multispectral gamma correction (ODMGC)

Low contrast enhancement technique for color images using interval-valued intuitionistic fuzzy sets with contrast limited adaptive histogram equalization

Performance Comparison of Firefly and Cuckoo Search Algorithms in Optimal Thresholding of Cancer Cell Images

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