An optimal statistical feature‐based transformation function for enhancement of retinal images using adaptive enhanced leader particle swarm optimization

Mahapatra, Sakambhari; Agrawal, Sanjay

doi:10.1002/ima.22767

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Numerous new algorithms are being developed as swarm intelligence algorithms continue to evolve. Furthermore, traditional swarm intelligence algorithms, such as [19][20][21], are being optimized and improved. Emerging algorithms have also found applications in image enhancement because of their outstanding performance, such as the marine predator algorithm (MPA) [22] and the salp swarm optimization algorithm (SSOA) [23].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Swarming Algorithm for Adaptive Enhancement of Low-Illumination Images

Zhang,

Liu,

2024

Symmetry

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This paper presents an improved swarming algorithm that enhances low-illumination images. The algorithm combines a hybrid Harris Eagle algorithm with double gamma (IHHO-BIGA) and incomplete beta (IHHO-NBeta) functions. This paper integrates the concept of symmetry into the improvement steps of the image adaptive enhancement algorithm. The enhanced algorithm integrates chaotic mapping for population initialization, a nonlinear formula for prey energy calculation, spiral motion from the black widow algorithm for global search enhancement, a nonlinear inertia weight factor inspired by particle swarm optimization, and a modified Levy flight strategy to prevent premature convergence to local optima. This paper compares the algorithm’s performance with other swarm intelligence algorithms using commonly used test functions. The algorithm’s performance is compared against several emerging swarm intelligence algorithms using commonly used test functions, with results demonstrating its superior performance. The improved Harris Eagle algorithm is then applied for image adaptive enhancement, and its effectiveness is evaluated on five low-illumination images from the LOL dataset. The proposed method is compared to three common image enhancement techniques and the IHHO-BIGA and IHHO-NBeta methods. The experimental results reveal that the proposed approach achieves optimal visual perception and enhanced image evaluation metrics, outperforming the existing techniques. Notably, the standard deviation data of the first image show that the IHHO-NBeta method enhances the image by 8.26%, 120.91%, 126.85%, and 164.02% compared with IHHO-BIGA, the single-scale Retinex enhancement method, the homomorphic filtering method, and the limited contrast adaptive histogram equalization method, respectively. The processing time of the improved method is also better than the previous heuristic algorithm.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Swarming Algorithm for Adaptive Enhancement of Low-Illumination Images

Zhang,

Liu,

2024

Symmetry

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A comprehensive review of artificial intelligence models for screening major retinal diseases

Hassan,

Raja,

Hassan

et al. 2024

Artif Intell Rev

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This paper provides a systematic survey of artificial intelligence (AI) models that have been proposed over the past decade to screen retinal diseases, which can cause severe visual impairments or even blindness. The paper covers both the clinical and technical perspectives of using AI models in hosipitals to aid ophthalmologists in promptly identifying retinal diseases in their early stages. Moreover, this paper also evaluates various methods for identifying structural abnormalities and diagnosing retinal diseases, and it identifies future research directions based on a critical analysis of the existing literature. This comprehensive study, which reviews both the conventional and state-of-the-art methods to screen retinopathy across different modalities, is unique in its scope. Additionally, this paper serves as a helpful guide for researchers who want to work in the field of retinal image analysis in the future.

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A Review on Retinal Blood Vessel Enhancement and Segmentation Techniques for Color Fundus Photography

Mahapatra,

Agrawal,

Mishro

et al. 2024

Crit Rev Biomed Eng

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The retinal image is a trusted modality in biomedical image-based diagnosis of many ophthalmologic and cardiovascular diseases. Periodic examination of the retina can help in spotting these abnormalities in the early stage. However, to deal with today's large population, computerized retinal image analysis is preferred over manual inspection. The precise extraction of the retinal vessel is the first and decisive step for clinical applications. Every year, many more articles are added to the literature that describe new algorithms for the problem at hand. The majority of the review article is restricted to a fairly small number of approaches, assessment indices, and databases. In this context, a comprehensive review of different vessel extraction methods is inevitable. It includes the development of a first-hand classification of these methods. A bibliometric analysis of these articles is also presented. The benefits and drawbacks of the most commonly used techniques are summarized. The primary challenges, as well as the scope of possible changes, are discussed. In order to make a fair comparison, numerous assessment indices are considered. The findings of this survey could provide a new path for researchers for further work in this domain.

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An optimal statistical feature‐based transformation function for enhancement of retinal images using adaptive enhanced leader particle swarm optimization

Cited by 3 publications

References 27 publications

A Hybrid Swarming Algorithm for Adaptive Enhancement of Low-Illumination Images

A Hybrid Swarming Algorithm for Adaptive Enhancement of Low-Illumination Images

A comprehensive review of artificial intelligence models for screening major retinal diseases

A Review on Retinal Blood Vessel Enhancement and Segmentation Techniques for Color Fundus Photography

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