An objective method to identify optimum clip-limit and histogram specification of contrast limited adaptive histogram equalization for MR images

Joseph, Justin; Jayaraman, Srinivasan; Periyasamy, R.; Renuka, S.

doi:10.1016/j.bbe.2016.11.006

Cited by 57 publications

(30 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to the subjective evaluation, quantitative measures are also crucial in comparing the performance of different image enhancement approaches. In this paper, the product of mean gradient and mean structural similarity (PMGSIM) given by Equation (17), the peak signal to noise ratio (PSNR) [ 59 , 60 , 74 ], the information entropy (IE) [ 53 , 68 ], the absolute mean brightness error (AMBE) [ 59 , 60 , 74 ], and the local contrast (LC) [ 75 ] values are employed here for the objective evaluation. Assuming that the size of the original image X is M × N , and Y is the enhanced image.…”

Section: Resultsmentioning

confidence: 99%

“…Hence, MSSIM is employed. It is a quality assessment for measuring the similarity between two images [ 73 , 74 , 76 ]. Suppose x and y are two nonnegative image signals for calculating the MSSIM, first we need to calculate SSIM (structural similarity index) [ 76 ] using

where the terms μ x and μ y , are the mean intensity, σ x and σ y are the standard deviation and σ xy is the covariance of images x and y , respectively, and c 1 , c 2 are the constant values.…”

Section: The Proposed Contrast Enhancement Methodsmentioning

confidence: 99%

“…In addition, HE may also cause loss of details since some gray levels with a smaller proportion pixel number are combined to form a certain gray level [ 49 ]. To avoid shortcomings of the classic HE, many techniques and improved algorithms on the basis of histogram equalization have been proposed in the past and have been widely utilized in the field of image enhancement [ 41 , 42 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 ]. Since preservation of the original brightness is crucial to avoid undesirable artifacts, the brightness preserving bi-histogram equalization (BBHE) [ 50 ] is developed to preserve the original brightness to a certain extent by individually equalizing two sub-histograms based on the mean value of the images.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A POSHE-Based Optimum Clip-Limit Contrast Enhancement Method for Ultrasonic Logging Images

Zhang

Celenk

et al. 2018

Sensors

View full text Add to dashboard Cite

Enabled by piezoceramic transducers, ultrasonic logging images often suffer from low contrast and indistinct local details, which makes it difficult to analyze and interpret geologic features in the images. In this work, we propose a novel partially overlapped sub-block histogram-equalization (POSHE)-based optimum clip-limit contrast enhancement (POSHEOC) method to highlight the local details hidden in ultrasonic well logging images obtained through piezoceramic transducers. The proposed algorithm introduces the idea of contrast-limited enhancement to modify the cumulative distribution functions of the POSHE and build a new quality evaluation index considering the effects of the mean gradient and mean structural similarity. The new index is designed to obtain the optimal clip-limit value for histogram equalization of the sub-block. It makes the choice of the optimal clip-limit automatically according to the input image. Experimental results based on visual perceptual evaluation and quantitative measures demonstrate that the proposed method yields better quality in terms of enhancing the contrast, emphasizing the local details while preserving the brightness and restricting the excessive enhancement compared with the other seven histogram equalization-based techniques from the literature. This study provides a feasible and effective method to enhance ultrasonic logging images obtained through piezoceramic transducers and is significant for the interpretation of actual ultrasonic logging data.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: The Proposed Contrast Enhancement Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A POSHE-Based Optimum Clip-Limit Contrast Enhancement Method for Ultrasonic Logging Images

Zhang

Celenk

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…Without clip boundaries, adaptive equalization histogram techniques can produce results that, in some cases, are worse than the original image. [7] "Distribution" defines the distribution that adapthisteq uses as a basis for creating contrast transformation functions. The distribution you choose should depend on the type of input image.…”

Section: Contrast-limited Adaptive Histogram Equalizationmentioning

confidence: 99%

Automated Contrast Improvement of X-Ray Image Using Otsu Threshold on Contrast-Limited Adaptive Histogram Equalization Algorithm

Riadi¹,

Wijayanti²,

Sugiharto³

et al. 2018

Proceedings of the the 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus

View full text Add to dashboard Cite

In the medical world, X-ray image quality is still a serious concern. The quality of X-ray image results is represented in the X-ray image contrast range. Correction of contrast ranges can be done computationally to improve X-ray image quality. From the problem then needed a method to improve result of X-ray image quality. In this research proposed OTSU thresholding modified into the Contrast-Limited Adaptive Histogram Equalization algorithm as a substitute for cliplimit value to improve the contrast quality of X-ray Image results which will then be measured with Mean Square Error to find out how much error value is generated.

show abstract

“…For each sub-frame compute the histogram and the highest peak value. Determine the nominal clipping limit[ 13].4. For each gray level bin in the histogram do the…”

mentioning

confidence: 99%

Feature Enhancement in Medical Ultrasound Videos Using Multifractal and Contrast Adaptive Histogram Equalization Techniques

Singh

Mukundan

Ryke

2019

2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)

View full text Add to dashboard Cite

Speckle noise reduction algorithms are extensively used in the field of ultrasound image analysis with the aim of improving image quality and diagnostic accuracy. However, significant speckle filtering induces blurring, and this would require enhancement of features and fine details. In this paper, we consider the applications of multifractal features and contrast limit adaptive histogram equalization method for improving texture features, contrast, resolvable details, and image structures to which the human visual system is sensitive in ultrasound video frames. The experimental analysis considered various types of ultrasound video scans of the human anatomy e.g. breast cancer, uterine fibroids, transvaginal ovary, ovarian cyst, heart, and chest pleural effusion scan. Subjective assessments by four radiologists and experimental validation using three quality metrics clearly indicate that the proposed algorithm is able to reduce speckle effectively while preserving essential information and enhancing the overall visual quality.

show abstract

An objective method to identify optimum clip-limit and histogram specification of contrast limited adaptive histogram equalization for MR images

Cited by 57 publications

References 9 publications

A POSHE-Based Optimum Clip-Limit Contrast Enhancement Method for Ultrasonic Logging Images

A POSHE-Based Optimum Clip-Limit Contrast Enhancement Method for Ultrasonic Logging Images

Automated Contrast Improvement of X-Ray Image Using Otsu Threshold on Contrast-Limited Adaptive Histogram Equalization Algorithm

Feature Enhancement in Medical Ultrasound Videos Using Multifractal and Contrast Adaptive Histogram Equalization Techniques

Contact Info

Product

Resources

About