Contrast enhancement of infrared images using Adaptive Histogram Equalization (AHE) with Contrast Limited Adaptive Histogram Equalization (CLAHE)

Abood, Loay Kadom

doi:10.30723/ijp.v16i37.84

Cited by 14 publications

(5 citation statements)

References 7 publications

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“…(µ β ) B are the mean of the blood image along the three axes l, α, and β. Then, using factors based on the corresponding standard deviations, blood image points are scaled by making up the synthetic image as in (4), (5), and (6). [14] where lˊ, α ˊ, β ˊ are the three axes of the color-corrected image, (µ l ) t, (µ α ) t, and (µ β ) t are the mean of the template along the three axes l α β , and σ l t, σ l B , σ α t, σ α B, σ β t, and…”

Section: A Color Correctionmentioning

confidence: 99%

“…In [4] a variety of WBCs segmentation and counting approaches was employed, including k-mean clustering, and the expectation-maximization algorithm. A framework for separation of the nucleus and cytoplasm of WBCs employing active contour, snake algorithm, and Zack thresholding was introduced in [5]. For WBCs segmentation and border detection of cells in images of peripheral blood smear slides, Otsu adaptive thresholding, and watershed transform were developed in [6].…”

Section: Introductionmentioning

confidence: 99%

“…Although there are several methods for WBCs counting [4][5][6][7][8][9][10][11][12][13], there are still some limitations in these methods such as the appearance of undesirable regions that emerge during the segmentation process. Additionally, overlapping cells are incorrectly counted as one cell.…”

mentioning

confidence: 99%

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Segmentation and Counting of White Blood Cells Using Image Processing Techniques

Tawfeek,

Y. Makkey,

A. Abdelrahman

2024

Menoufia Journal of Electronic Engineering Research

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The counting of white blood cells (WBCs) is an extremely essential measurement parameter to diagnose some particular diseases and identify various infections that are concealed within the human body. Within the hospital, manual counting of WBCs is time-consuming, laborious, and needs experienced experts for accurate results. Thus, computer-aided diagnosis methods can help pathologists to perform accurate counting with less effort. To achieve this, a new method for WBCs counting based on incorporating the marker-controlled watershed algorithm with morphological filters for a microscopic blood sample image is proposed in this paper. To begin with, color correction is applied to standardize the amount of color intensity in the original blood-smeared image. Segmentation of white blood cells is then carried out using huesaturation-value (HSV) model color analysis with the Otsu threshold. Noise and undesirable regions that emerge during the segmentation process are removed using morphological filters. For overlapping WBCs, an effective segmentation method based on a watershed algorithm is introduced to overcome the limitations in the existing WBCs counting methods. Images from the ALL_IDB1 dataset are utilized to apply and evaluate the proposed approach. An accuracy of 95% is achieved in the counting of WBCs. The evaluation results reveal that the proposed method outperforms the accuracy of the traditional methods and overcomes their shortages.

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Section: A Color Correctionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Segmentation and Counting of White Blood Cells Using Image Processing Techniques

Tawfeek,

Y. Makkey,

A. Abdelrahman

2024

Menoufia Journal of Electronic Engineering Research

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“…The shape of a graylevel histogram indicates the general appearance of an image [17]. For dark images, the histogram elements are concentrated on the low (dark) side of the intensity scale, while for light images, these elements of the histogram are concentrated on the high side of the scale [17]. A low-contrast image has a narrow histogram typically in the middle of the intensity scale.…”

Section: Histogram Processingmentioning

confidence: 99%

Mask Laws to study Texture Features of the Kidney Infection

Hassan,

Ali,

Shehab

et al. 2023

Iraqi Journal of Science

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This paper aims to early study of detection and diagnosis of kidney tumors and kidney stones using Computed Tomography Scanning CT scan images by digital image processing. Computerized Axial Tomography (CAT) is a special medical imaging technique that provides images with 3D, including much information about the body's construction consisting of bones and organs. A C.T scan uses X-rays to create cross-sectional images of the body and gives the doctor a full explanation of the diagnosis of the situation through the examination. It has been used in five cases of kidney images, including healthy, stones, tumors (cancer), cystic and renal fibrosis. The masking procedure is used to separate the required C.T. images from undesirable ones. The segmentation processes which was used in this study is a thresholding algorithm, applied on C.T. images, used to detect unhealthy tissues (tumor and stone). The results showed that the system is able to distinguish between different tumors of the kidney of cases mentioned above. It's important for the instructor to determine the area of the tumor and stone to avoid damaging the healthy tissue during the treatment. The histogram which was used to study the differences between the healthy and unhealthy depending on the intensity of the image

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“…1 Enhanced Dataset Utilization with Image Enhancement Techniques: We enhance the brain tumor dataset using advanced image enhancement techniques, including Homomorphic Filtering (HF) [ 8 ], All Channels Contrast Limited Adaptive Histogram Equalization (CLAHE) [ 9 ], and Unsharp Masking (UM) [ 10 ]. These methods are chosen to enhance image details, mitigate noise levels, and improve contrast in brain tumor images [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Brain tumor classification in VIT-B/16 based on relative position encoding and residual MLP

Hong,

Wu,

Zhu

et al. 2024

PLoS ONE

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Brain tumors pose a significant threat to health, and their early detection and classification are crucial. Currently, the diagnosis heavily relies on pathologists conducting time-consuming morphological examinations of brain images, leading to subjective outcomes and potential misdiagnoses. In response to these challenges, this study proposes an improved Vision Transformer-based algorithm for human brain tumor classification. To overcome the limitations of small existing datasets, Homomorphic Filtering, Channels Contrast Limited Adaptive Histogram Equalization, and Unsharp Masking techniques are applied to enrich dataset images, enhancing information and improving model generalization. Addressing the limitation of the Vision Transformer’s self-attention structure in capturing input token sequences, a novel relative position encoding method is employed to enhance the overall predictive capabilities of the model. Furthermore, the introduction of residual structures in the Multi-Layer Perceptron tackles convergence degradation during training, leading to faster convergence and enhanced algorithm accuracy. Finally, this study comprehensively analyzes the network model’s performance on validation sets in terms of accuracy, precision, and recall. Experimental results demonstrate that the proposed model achieves a classification accuracy of 91.36% on an augmented open-source brain tumor dataset, surpassing the original VIT-B/16 accuracy by 5.54%. This validates the effectiveness of the proposed approach in brain tumor classification, offering potential reference for clinical diagnoses by medical practitioners.

show abstract

Contrast enhancement of infrared images using Adaptive Histogram Equalization (AHE) with Contrast Limited Adaptive Histogram Equalization (CLAHE)

Cited by 14 publications

References 7 publications

Segmentation and Counting of White Blood Cells Using Image Processing Techniques

Segmentation and Counting of White Blood Cells Using Image Processing Techniques

Mask Laws to study Texture Features of the Kidney Infection

Brain tumor classification in VIT-B/16 based on relative position encoding and residual MLP

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