A novel medical image enhancement algorithm based on CLAHE and pelican optimization
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| Pubblicato in: | Multimedia Tools and Applications vol. 83, no. 42 (Dec 2024), p. 90069 |
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| Pubblicazione: |
Springer Nature B.V.
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| Soggetti: | |
| Accesso online: | Citation/Abstract Full Text - PDF |
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| 024 | 7 | |a 10.1007/s11042-024-19070-6 |2 doi | |
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| 245 | 1 | |a A novel medical image enhancement algorithm based on CLAHE and pelican optimization | |
| 260 | |b Springer Nature B.V. |c Dec 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Medical image enhancement is considered a challenging image-processing framework because the low quality of images resulting after acquisition and transmission seriously affects the clinical diagnosis and observation. In order to improve the medical image visual quality, a novel medical image enhancement algorithm that is based on contrast adaptive histogram equalization and pelican optimization algorithm is proposed in this work. The estimation process using our proposed model improves the efficiency of the operation and provides superior results in terms of image quality and contrast. There are three steps in the enhancement process. The primary step includes medical image generation using a Text-to-image generative model. Secondly, the estimation of the clip-limit, which controls the enhancing performance. Finally, the operation of enhancing the medical images using our proposed method. The simulation experiments prove that our proposed algorithm achieves superior performance qualitatively and quantitatively, compared with the state-of-the-art experimental methods, Upon a thorough examination and comparative analysis of performance parameters. Furthermore, the advantageous characteristic of this algorithm is its applicability in multiple types of images. Improving the quality of the medical images using our algorithm allows us to attain a superior visual impact on the processed image, and to increase the rate of conformity in the clinical diagnosis. Our proposed model illustrates the structure and forms of relevant details, contained in the medical images. This leads to an increase in overall contrast and enhances visual perception. | |
| 653 | |a Performance enhancement | ||
| 653 | |a Visual perception | ||
| 653 | |a Image enhancement | ||
| 653 | |a Visual observation | ||
| 653 | |a Medical imaging | ||
| 653 | |a Optimization | ||
| 653 | |a Impact analysis | ||
| 653 | |a Image transmission | ||
| 653 | |a Diagnosis | ||
| 653 | |a Image acquisition | ||
| 653 | |a Algorithms | ||
| 653 | |a Image quality | ||
| 653 | |a Image processing | ||
| 653 | |a Visual perception driven algorithms | ||
| 653 | |a Adaptive algorithms | ||
| 773 | 0 | |t Multimedia Tools and Applications |g vol. 83, no. 42 (Dec 2024), p. 90069 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3149797872/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3149797872/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |