Enhancement and segmentation of medical images through pythagorean fuzzy sets-An innovative approach

Premalatha, R.; Palanisamy, D.

doi:10.1007/s00521-022-07043-5

Cited by 17 publications

(18 citation statements)

References 36 publications

(31 reference statements)

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“…There are three types of linguistic hedges that are going to be discussed. CON from concentration, DIL from dilation, and INT from intensification [4,26,27]. Their mathematical representations are as follows:…”

Section: Linguistic Hedgesmentioning

confidence: 99%

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Advanced Fuzzy Sets and Genetic Algorithm Optimizer for Mammographic Image Enhancement

Dounis,

Avramopoulos,

Kallergi

2023

Electronics

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A well-researched field is the development of Computer Aided Diagnosis (CADx) Systems for the benign-malignant classification of abnormalities detected by mammography. Due to the nature of the breast parenchyma, there are significant uncertainties about the shape and geometry of the abnormalities that may lead to an inaccurate diagnosis. These same uncertainties give mammograms a fuzzy character that is essential to the application of fuzzy processing. Fuzzy set theory considers uncertainty in the form of a membership function, and therefore fuzzy sets can process imperfect data if this imperfection originates from vagueness and ambiguity rather than randomness. Fuzzy contrast enhancement can improve edge detection and, by extension, the quality of related classification features. In this paper, classical (Linguistic hedges and fuzzy enhancement functions), advanced fuzzy sets (Intuitionistic fuzzy set (ΙFS), Pythagorean fuzzy set (PFS), and Fermatean fuzzy sets (FFS)), and a Genetic Algorithm optimizer are proposed to enhance the contrast of mammographic features. The advanced fuzzy sets provide better information on the uncertainty of the membership function. As a result, the intuitionistic method had the best overall performance, but most of the techniques could be used efficiently, depending on the problem that needed to be solved. Linguistic methods could provide a more manageable way of spreading the histogram, revealing more extreme values than the conventional methods. A fusion technique of the enhanced mammography images with Ordered Weighted Average operators (OWA) achieves a good-quality final image.

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Section: Linguistic Hedgesmentioning

confidence: 99%

“…Membership µ expresses the foreground of the grayscale image, non-membership ν the background, and indeterminacy π the edges. These ex-pressions have to be obtained by executing the functions µ, ν, and π. µ P (s) → [0, 1] and v P (s) → [0, 1] are the membership and non-membership, respectively [26,29]. The PFS is:…”

Section: Pythagorean Fuzzy Setsmentioning

confidence: 99%

Advanced Fuzzy Sets and Genetic Algorithm Optimizer for Mammographic Image Enhancement

Dounis,

Avramopoulos,

Kallergi

2023

Electronics

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“…Then, the contrast stretching is applied on modified PFI using INT operator 30 , and is mathematically represented as:…”

Section: Image Enhancementmentioning

confidence: 99%

Enhanced multimodal medical image fusion based on Pythagorean fuzzy set- An innovative approach

Haribabu

Guruviah

2023

Preprint

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The primary goal of this article is to combine multi-modality medical images into a single output image in order to obtain superior information and better visual appearance without any vagueness and uncertainties, which is suitable for better diagnosis. The complexity of medical images is higher, and many researchers applied various soft computing methods to process them. Pythagorean fuzzy set (PFS) is more suitable for medical images because it considers more uncertainties. In this article, a new method, Pythagorean fuzzy set-based medical image fusion is proposed. Initially, the source images are decomposed into base and detail layers using the two-layer decomposition method, and these layers contain structural and edge details of the source images. To preserve more edge details and clarity, a spatial frequency based fusion rule is employed for detail layers. The base layer images have low contrast, to enhance this; it is converted into Pythagorean fuzzy images (PFIs) with the help of optimum value, which can be generated by Pythagorean fuzzy entropy (PFE). Then, the two pythagorean fuzzy images are decomposed into image blocks, and then perform blackness and whiteness count fusion rule. Finally, the enhanced fused image is obtained by reconstructions of PFI blocks and performs the defuzzification process. The efficiency of the proposed fusion method proves that in terms of both visually and quantitatively compared to other existing fusion methods. The proposed method is tested on different datasets with various quality metrics, which produces an enhanced fused image without artifacts and uncertainties

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“…Due to limited training samples, learning machines based on the principle of minimising experimental risk often suffer from a lack of generalisability in practical applications [7][8]. Support vector machine methods based on statistical learning theory use standard optimisation parameters to minimise structural risk and use larger interval factors to control the training process of the learning machine [9][10].…”

Section: Support Vector Machinesmentioning

confidence: 99%

The Application of Support Vector Machines in Medical Image Segmentation

2022

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Medical image segmentation is an interdisciplinary research area and an important application of graphics computing and image processing in biomedical engineering. Medical image segmentation has important applications in diagnostic medicine, surgical planning and anatomy teaching. The aim of this paper is to investigate support vector machine based medical image segmentation. The main algorithms of image segmentation, the problems of Mr image segmentation, the development trend of medical image segmentation and the current status of support vector machine research are described. Different kernel functions are selected to generate different support vector machine classifiers for segmentation of MR brain images using Mr images as the research object, and the experimental results show that the cross-validation of Gaussian radial basis kernel functions has a high correct rate.

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Enhancement and segmentation of medical images through pythagorean fuzzy sets-An innovative approach

Cited by 17 publications

References 36 publications

Advanced Fuzzy Sets and Genetic Algorithm Optimizer for Mammographic Image Enhancement

Advanced Fuzzy Sets and Genetic Algorithm Optimizer for Mammographic Image Enhancement

Enhanced multimodal medical image fusion based on Pythagorean fuzzy set- An innovative approach

The Application of Support Vector Machines in Medical Image Segmentation

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