Contrast Enhancement of Medical Images Using Statistical Methods with Image Processing Concepts

Al-Ameen, Zohair

doi:10.1109/iec49899.2020.9122925

Cited by 16 publications

(9 citation statements)

References 21 publications

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“…In addition, only MedGA and DPSF techniques were adequate to enhanced FA images efficiently and accurately. LHE technique performed the least in all of the imaging technique as compared with MedGA [38], DPSF [39], Zohair et al [40], and HE [2]. The proposed algorithm performed better than these techniques (MedGA [38], DPSF [39], Zohair et al [40], HE [2], and LHE [6]).…”

Section: Subjective-baed Assessmentmentioning

confidence: 90%

“…4, we compared the proposed LTF-NSI with the currently published works namely, MedGA [38], DPSF [39], Zohair et al [40], and two well-established conventional methods such as HE [2], and LHE [6]. We observed that for the X-ray and CT imaging modalities, HE technique performed better than all the currently published works MedGA [38], DPSF [39], Zohair et al [40], and LHE [6]. For the OCTA imaging modality, DPSF [39] performed best and MedGA [38] outperformed all the other techniques (DPSF, Zohair, HE, and LHE) based on FA imaging modality.…”

Section: Subjective-baed Assessmentmentioning

confidence: 99%

“…In general, we compare the proposed LTF-NSI with ten image enhancement techniques, that consist of eight stateof-the-art techniques (Chitchian et al [13], Anantrasirichai et al [14], Liu et al [15], Li et al [16], Ying et al [17], MedGA [38], DPSF [39], and Zohair et al [40]) and two well-established conventional techniques (HE [2] and LHE [6]). Among these techniques, HE outperformed other techniques in X-ray, followed by DPSF, Zohair, and MedGA, while other techniques follow.…”

Section: Subjective-baed Assessmentmentioning

confidence: 99%

See 2 more Smart Citations

LTF-NSI: a novel local transfer function based on neighborhood similarity index for medical image enhancement

Okuwobi

Ding

Wan

et al. 2022

Complex Intell. Syst.

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Medical image is an essential tool used in quantitative and qualitative evaluation of different diseases. Medical imaging methods such as fluorescein angiography (FA), optical coherence tomography angiography (OCTA), computed tomography (CT), optical coherence tomography (OCT), and X-ray are used for diagnosis. These imaging modalities suffer from low contrast, which leads to deterioration in the image quality. Consequently, this causes limitation in the usage of medical images in clinical routine and hindered its potential by depriving clinicians from assessing useful information that are needed in disease monitoring, treatment, progression, and decision-making. To overcome this limitation, we propose a novel local transfer function for medical image enhancement algorithm using the pixel neighborhood constraint. The proposed algorithm uses block-wise intensity distribution to generate the regional similarity index. The regional similarity index transformed each centered pixel in the block, to generate a new similarity image. An intuitive optimization algorithm is utilized to optimize the proposed algorithm parameters. Experimentation results show that the proposed LTF-NSI performs better than the state-of-the-art methods and improves the interpretability and perception of the medical images, which can provide clinicians and computer vision program with good quantitative and qualitative information.

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Section: Subjective-baed Assessmentmentioning

confidence: 90%

Section: Subjective-baed Assessmentmentioning

confidence: 99%

Section: Subjective-baed Assessmentmentioning

confidence: 99%

See 1 more Smart Citation

LTF-NSI: a novel local transfer function based on neighborhood similarity index for medical image enhancement

Okuwobi

Ding

Wan

et al. 2022

Complex Intell. Syst.

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“…Contrast is an essential parameter determining image resolution [34]. To further improve image quality, contrast was optimized by histogram adjustment using the Matlab ® Image Processing Toolbox™ [29].…”

Section: Histogram Adjustmentmentioning

confidence: 99%

An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction

et al. 2022

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Despite the use of new techniques on embryo selection and the presence of equipment on the market, such as EmbryoScope® and Geri®, which help in the evaluation of embryo quality, there is still a subjectivity between the embryologist’s classifications, which are subjected to inter- and intra-observer variability, therefore compromising the successful implantation of the embryo. Nonetheless, with the acquisition of images through the time-lapse system, it is possible to perform digital processing of these images, providing a better analysis of the embryo, in addition to enabling the automatic analysis of a large volume of information. An image processing protocol was developed using well-established techniques to segment the image of blastocysts and extract variables of interest. A total of 33 variables were automatically generated by digital image processing, each one representing a different aspect of the embryo and describing a different characteristic of the blastocyst. These variables can be categorized into texture, gray-level average, gray-level standard deviation, modal value, relations, and light level. The automated and directed steps of the proposed processing protocol exclude spurious results, except when image quality (e.g., focus) prevents correct segmentation. The image processing protocol can segment human blastocyst images and automatically extract 33 variables that describe quantitative aspects of the blastocyst’s regions, with potential utility in embryo selection for assisted reproductive technology (ART).

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“…Hence, an automatic linear contrast stretching (ALCS) approach is applied to provide a full-scale stretch by reallocating the intensities to the full natural interval. The used stretching approach can be computed as follows [31]:…”

Section: Proposed Techniquementioning

confidence: 99%

Satellite Image Enhancement Using an Ameliorated Balance Contrast Enhancement Technique

Al-Ameen¹

2020

Self Cite

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The quality of satellite images is frequently degraded by the low-contrast effect. Therefore, different research works have been developed to deal with this undesirable effect. Still, no conclusive verdicts have been reached and the problem remains open for research. Hence, an ameliorated balance contrast enhancement technique using a parabolic function (ABCETP) is proposed in this article to improve the quality in terms of brightness, contrast, and colors. The original BCETP works by determining a parabolic function with three coefficients. However, the proposed ABCETP works by computing a modified parabolic function with one modified and two unmodified coefficients, as well as, it utilizes two additional methods to produce adequate-quality results. The proposed ABCETP is examined with numerous real contrast-distorted images, compared against different contrast enhancement methods and the results are assessed with two specialized quality appraisal metrics. Empirical results obtained from conducting various comparisons and experiments revealed the favorability of ABCETP, wherein it outputted images with better-perceived quality and outperformed the comparative methods in several aspects.

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Contrast Enhancement of Medical Images Using Statistical Methods with Image Processing Concepts

Cited by 16 publications

References 21 publications

LTF-NSI: a novel local transfer function based on neighborhood similarity index for medical image enhancement

LTF-NSI: a novel local transfer function based on neighborhood similarity index for medical image enhancement

An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction

Satellite Image Enhancement Using an Ameliorated Balance Contrast Enhancement Technique

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