Medical image illumination enhancement and sharpening by using stationary wavelet transform

Rasti, Pejman; Daneshmand, Morteza; Alisinanoglu, F; Özçinar, Çağrı; Anbarjafari, Gholamreza

doi:10.1109/siu.2016.7495700

Cited by 19 publications

(5 citation statements)

References 23 publications

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“…Two 512 × 512 medical images with 256 gray level were selected as references. In the experiment, the algorithm was compared with other algorithms, including medical image illumination enhancement and sharpening by using stationary wavelet transform (MIIESUSWT), 16 bi‐histogram equalization using modified histogram bins (BHEMHB), 17 medical image enhancement based on adaptive histogram equalization and contrast stretching (MIEAHECS), 20 and a method of improved fuzzy contrast combined adaptive threshold in NSCT for medical image enhancement (NSCT) 32 . At the same time, information entropy (H), 32 PSNR, 32 result similarity (SSIM), 32 and root mean square error (RMSE) 33 .…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…In recent years, researchers have made many improvements to the histogram equalization algorithm. For example, in document, 16 a medical image enhancement algorithm based on adaptive histogram equalization and contrast stretching was proposed, which improves the image contrast and avoids serious image distortion. In Reference 17 , a modified double histogram equalization algorithm was proposed, which retains the information entropy of the image.…”

Section: Introductionmentioning

confidence: 99%

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An efficient and high quality medical CT image enhancement algorithm

Yang

Kasabov

2020

Int J Imaging Syst Tech

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Aiming at the process of medical diagnosis, many problems such as unclear images and low contrast are often caused by noise and interference in the process of medical image acquisition and transmission. This article proposes a new image enhancement method that combines the wavelet domain with the spatial domain. First, we input two identical images (Both of the identical images are original images.) in which the first image is enhanced by histogram equalization. Then, the two images are divided into four sub‐images by a two‐dimensional wavelet transform. The average of the low‐frequency coefficients of the low‐frequency sub‐images of the two images is taken as the low‐frequency coefficients of the final reconstruction. Second, aiming at the problem that the contrast may be too low, the fourth high‐frequency sub‐image is blurred (sharpened) twice. The fourth high‐frequency sub‐image after blurring is denoised by median filtering. Finally, the four sub‐images are fused to obtain the enhanced image. The experimental results show that the peak signal‐to‐noise ratio, structural similarity, and processing time of the proposed algorithm are better than those of other contrast algorithms, especially the processing time. These objective indicators show that the proposed algorithm can not only effectively suppress noise but also significantly enhance the contrast. Subjectively, compared with other algorithms, the proposed algorithm achieves a better visual effect and greatly reduces the processing time.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An efficient and high quality medical CT image enhancement algorithm

Yang

Kasabov

2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…At present, image enhancement methods based on wavelet, histogram, singular value decomposition, and gamma correction have developed rapidly, which have produced considerable effects on improving contrast and improving image quality. The method proposed by Pejman Rasti et al uses SWT to decompose the image to enhance the brightness of the low-frequency sub-band, and adds the high-frequency sub-band to the edge of the image to enhance the contrast of medical images with poor illumination [18] . R. Priyadharsini et al performed Laplacian filtering on low-frequency subbands based on SWT, and superimposed non-stationary wavelet reconstruction to obtain high-contrast images [19] .…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Traditional pattern enhancement based on improved singular value decomposition and gamma function in the frequency domain

Yan

Yuan

et al. 2023

Preprint

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Aiming at the problems of blurring and distortion of traditional patterns, an enhancement method to improve resolution and contrast is proposed.First, the discrete wavelet transform, stationary wavelet transform, and interpolation algorithm are used to obtain high-resolution images of traditional patterns. Then, the improved singular value matrix coefficients and the reconstructed gamma function are used to enhance the image contrast to obtain high-resolution and contrast-enhanced patterns. The experimental results show that the evaluation indexes such as mean square error, peak signal-to-noise ratio, and structural similarity corresponding to this method are improved compared with other comparison methods. It can effectively improve the quality of traditional patterns and is of great significance to the research on the restoration and protection of traditional patterns.

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“…For more details on the kernels that can be used and the formulas, the reader is referred to the classical textbook of Gonzalez and Woods [2007]. Another successful technique for image enhancement is the use of stationary wavelet transform where the high-frequency subbands are added to the original image [Rasti et al, 2016].…”

Section: I(x Y) = I(x Y) + K(i(x Y) -I Lp (X Y))mentioning

confidence: 99%

Basis of Image Analysis for Evaluating Cell Biomaterial Interaction Using Brightfield Microscopy

Uka

Halili

Polisi

et al. 2021

Cells Tissues Organs

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Medical imaging is a growing field that has stemmed from the need to conduct noninvasive diagnosis, monitoring, and analysis of biological systems. With the developments and advances in the medical field and the new techniques that are used in the intervention of diseases, very soon the prevalence of implanted biomedical devices will be even more significant. The implanted materials in a biological system are used in diverse fields, which require lengthy evaluation and validation processes. However, currently the evaluation of the toxicity of biomaterials has not been fully automated yet. Moreover, image analysis is an integral part of biomaterial research, but it is not within the core capacities of a significant portion of biomaterial scientists, which results in the use of predominantly ready-made tools. The detailed image analysis can be conducted once all the relevant parameters including the inherent characteristics of image acquisition techniques are considered. Herein, we cover the currently used image analysis-based techniques for assessment of biomaterial/cell interaction with a specific focus on unstained brightfield microscopy acquired mostly in but not limited to microfluidic systems, which serve as multiparametric sensing platforms for noninvasive experimental measurements. We present the major imaging acquisition techniques that enable point-of-care testing when incorporated with microfluidic cells, discuss the constraints enforced by the geometry of the system and the material that is analyzed, and the challenges that rise in the image analysis when unstained cell imaging is employed. Emerging techniques such as utilization of machine learning and cell-specific pattern recognition algorithms and potential future directions are discussed. Automation and optimization of biomaterial assessment can facilitate the discovery of novel biomaterials together with making the validation of biomedical innovations cheaper and faster.

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Medical image illumination enhancement and sharpening by using stationary wavelet transform

Cited by 19 publications

References 23 publications

An efficient and high quality medical CT image enhancement algorithm

An efficient and high quality medical CT image enhancement algorithm

Traditional pattern enhancement based on improved singular value decomposition and gamma function in the frequency domain

Basis of Image Analysis for Evaluating Cell Biomaterial Interaction Using Brightfield Microscopy

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