Retinex-Centered Contrast Enhancement Method for Histopathology Images with Weighted CLAHE

Rao, Karishma; Bansal, Manu; Kaur, Gagandeep

doi:10.1007/s13369-021-06421-w

Cited by 8 publications

(7 citation statements)

References 29 publications

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“…Universal Image Quality Index (UIQI) is a mathematical evaluation that considers image alteration in conjunction with illumination damage, contrast deformation, and covariance 50 . The following expression 22,51 is used to compute it.

UIQI = \frac{4 σ_{X, \overset{true¯}{X}} μ_{X} μ_{\overset{X}{true}}}{(σ_{X}^{2} + σ_{\overset{X}{true}}^{2}) (μ_{X}^{2} + μ_{\overset{X}{true}}^{2})},

where

σ_{X, \overset{true¯}{X}}

represents the correlation coefficients between

X

(original image) and

\overset{true¯}{X}

(enhanced image). Furthermore,

μ_{X}^{2}

and

μ_{\overset{X}{true}}^{2}

indicate the average values of

X

and

X

, respectively, while

σ_{X}^{2}

and

σ_{\overset{X}{true}}^{2}

depict the deviations

X

and

\overset{true¯}{X}

, respectively.…”

Section: Resultsmentioning

confidence: 99%

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An optimal system for increasing the contrast resolution qualities of histopathology images in the wavelet domain

Rao,

Bansal,

Kaur

2023

Int J Imaging Syst Tech

Self Cite

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In the field of histopathology, it has been observed that most images are of poor quality as a result of inadequate luminance and contrast. The first step in identifying and analyzing them is to enhance their quality while maintaining their inherent characteristics. This paper presents a novel method for enhancing histopathology images' important features, brightness preservation, and contrast by utilizing the adaptive intensity transformation gamma function in the wavelet domain. The RGB histopathological color image is first converted to the HSV color model. The V component is then stretched in order to compensate for any color distortion. In the proposed scheme, the stretched V component is split into four sub‐bands using discrete wavelet transform (DWT). Next, it determines the optimal value of the gamma correction function in the DWT lower sub‐band. A correction factor is applied using the singular value decomposition (SVD) technique to determine the optimal gamma function value using the nature‐inspired particle swarm optimization method (NIPSO). The corrected LL sub‐band is then applied to the inverse DWT (IDWT) together with the other unprocessed sub‐bands. In addition, the contrast of each feature is enhanced by employing an adaptive histogram equalization model that is contrast‐limited. The proposed method was evaluated through experiments and validations on a variety of histopathology images retrieved from multiple databases. Several quantitative studies have demonstrated that the proposed method outperforms existing enhancement techniques in terms of entropy, edge preservation index, contrast ratio, and Universal Image Quality Index (UIQI). This proposed technique increases contrast, while preserving the inherent characteristics of the original image, resulting in better quality histopathology images that can be used in disease diagnosis and inspection.

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UIQI = \frac{4 σ_{X, \overset{true¯}{X}} μ_{X} μ_{\overset{X}{true}}}{(σ_{X}^{2} + σ_{\overset{X}{true}}^{2}) (μ_{X}^{2} + μ_{\overset{X}{true}}^{2})},

where

σ_{X, \overset{true¯}{X}}

represents the correlation coefficients between

X

(original image) and

\overset{true¯}{X}

(enhanced image). Furthermore,

μ_{X}^{2}

and

μ_{\overset{X}{true}}^{2}

indicate the average values of

X

and

X

, respectively, while

σ_{X}^{2}

and

σ_{\overset{X}{true}}^{2}

depict the deviations

X

and

\overset{true¯}{X}

, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Universal Image Quality Index (UIQI) is a mathematical evaluation that considers image alteration in conjunction with illumination damage, contrast deformation, and covariance. 50 The following expression 22,51 is used to compute it.…”

Section: Universal Image Quality Indexmentioning

confidence: 99%

An optimal system for increasing the contrast resolution qualities of histopathology images in the wavelet domain

Rao,

Bansal,

Kaur

2023

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…The modified Gaussian surround function is as follows where M × N means image size, K is the normalization factor, and β is a tuning constant whose value satisfies β ≥ 0 . A modified Sigmoid function has been added to adjust the excess luminance of the developed SSR when β ≥ 2 and is described as follows 25 where f is the degraded image, α is the constant parameter that controls the enhancement of the function, and γ is an adjustment constant. And the value α is automatically calculated from the value β as follows where T is the adjustment value.…”

Section: Methodsmentioning

confidence: 99%

“…The SSR methods [24][25][26] often use a combination of Gaussian low-pass filtering and center surround algorithms to estimate the incident component and thus optimize the algorithm, which reduces the calculation time. It has been widely used to enhance the images and perform well.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic image positioning of a rail train number using speed-up robust features and stroke width transform

Xing

Zhang

Yao

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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The rail train number is the only sign information of the vehicle. It is very important to use the image processing method to locate the train number to improve the efficiency of rail train management. Therefore, an automatic image positioning framework for rail train numbers is developed. First, aiming at the problem of low contrast between the rail train number and vehicle body caused by insufficient illumination of the rail train in a tunnel and indoor, the improved single-scale Retinex algorithm based on brightness control is used to enhance the image, which provides the basis for subsequent train number image positioning. Then, a train number location algorithm based on speed-up robust features and stroke width transform is proposed to locate the train number accurately. The experimental results show that the accuracy of the developed method is 96.91% in the complex environment of uneven illumination and large distortion of rail train numbers.

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“…Many factors usually degrade histopathology images, resulting in low contrast images [31]. The preprocessing of histopathology images is the first stage in the detection of OSCC.…”

Section: Preprocessingmentioning

confidence: 99%

An image patch selection algorithm for the detection of Oral Squamous Cell Carcinoma using textural and morphological features

Subhija

Reju

2023

Preprint

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A significant proportion of cancer-related deaths are caused by oral cancer, and oral squamous cell carcinoma is prevalent. Systems for computer-aided diagnosis can lower subjective errors and assist the pathologist in making a more accurate diagnosis. Feature extraction from the whole histopathology images is difficult due to the structural variation of the oral tissue images. A new patch selection algorithm can be used to create and select image patches containing nuclei-specific information and extract their textural and morphological characteristics, improving cancer diagnosis. We extract the morphological characteristics of the nucleus from the selected patches using five pretrained networks. The texture from the regions of the selected patches is also extracted from the Haar wavelet decomposed components using the gray-level co-occurrence matrix. Then, we combine the textural and morphological features to create the final feature vector, followed by feature selection using an extra trees classifier. In order to detect oral squamous cell carcinoma, we examined the feasibility of using six classifiers, including voting classifier, logistic regression, random forest, Naive Bayes, K nearest neighbor, and support vector machine. The performance of the algorithm is evaluated using accuracy, precision, sensitivity, confusion matrix, ROC curves, and AUC values of characteristic curves. Together, ResNet 50 and the voting classifier produce results with a high accuracy of 97.66 % and a precision of 98.00 %. The suggested patch-based method outperforms the image based method and is accurate and efficient for identifying oral squamous cell carcinoma and will be a reliable and precise support tool for oral pathologists.

show abstract

Retinex-Centered Contrast Enhancement Method for Histopathology Images with Weighted CLAHE

Cited by 8 publications

References 29 publications

An optimal system for increasing the contrast resolution qualities of histopathology images in the wavelet domain

An optimal system for increasing the contrast resolution qualities of histopathology images in the wavelet domain

Automatic image positioning of a rail train number using speed-up robust features and stroke width transform

An image patch selection algorithm for the detection of Oral Squamous Cell Carcinoma using textural and morphological features

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