MRI brain image enhancement using Histogram Equalization techniques

Kaur, Hardeep; Rani, Jyoti

doi:10.1109/wispnet.2016.7566237

Cited by 56 publications

(28 citation statements)

References 2 publications

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“…The function

T ()(, r_{k})

which maps each pixel in the input image with intensity

r_{k}

into a corresponding pixel with a

s_{k}

level in the output image is known as histogram linearisation or histogram equalisation. This is represented through the following relation

s_{k} = T ()(, r_{k}) = ()(, l - 1) \sum_{j = 0}^{k} p_{r} ()(, r_{j}) = \frac{l - 1}{MN} \sum_{j = 0}^{k} n_{j}, k = 0, 1, \dots, l

This type of mapping causes the output image to have a uniform distribution of intensity values usually enhancing the contrast of the image with close contrast values spanning a wider range of intensity scales [68, 69].…”

Section: Proposed Methodsmentioning

confidence: 99%

Decision fusion using virtual dictionary‐based sparse representation for robust SAR automatic target recognition

Shafie¹,

Moallem²,

Sabahi³

2020

IET Radar, Sonar & Navigation

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“…The function

T ()(, r_{k})

which maps each pixel in the input image with intensity

r_{k}

into a corresponding pixel with a

s_{k}

level in the output image is known as histogram linearisation or histogram equalisation. This is represented through the following relation

s_{k} = T ()(, r_{k}) = ()(, l - 1) \sum_{j = 0}^{k} p_{r} ()(, r_{j}) = \frac{l - 1}{MN} \sum_{j = 0}^{k} n_{j}, k = 0, 1, \dots, l

Section: Proposed Methodsmentioning

confidence: 99%

Decision fusion using virtual dictionary‐based sparse representation for robust SAR automatic target recognition

Shafie¹,

Moallem²,

Sabahi³

2020

IET Radar, Sonar & Navigation

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“…Histogram equalization has been showing its vast contribution in improvement of image quality in numerous sectors like medical images [16], pattern imaging [4] and so on. Previously, the performances of different types of histogram equalization such as; Global Histogram Equalization, Local Histogram Equalization and Fast Quadratic Dynamic Histogram Equalization in peak signal to noise ratio was performed [17].…”

Section: Literature Reviewmentioning

confidence: 99%

“…cancer, soft tissue, stroke etc.) the performances of four types of histogram equalizations were granted [16]. Though the quality in enhanced color images are no better than the gray scale enhanced image quality, approaches were made with 3-D Color Histogram Equalization method [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Digital Image Enhancement in Matlab: An Overview on Histogram Equalization and Specification

Arif¹,

Khan²,

Siddique³

2019

Preprint

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This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

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“…In phase-I denoising of MRI image is done which then followed by Enhancement Technique. By using HE (Histogram Equalization) the MRI brain image enhancement is described in [5]. In [2] comparative study for contrast enhancement of brain tumour is studied.…”

Section: Introductionmentioning

confidence: 99%

Contrast Enhancementof MRI Images using AHE and CLAHE Techniques

Rubini*¹,

Pavithra²

2019

IJITEE

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Medical images require image enhancement, a category of image processing which provides better visualization that make diagnostic more accurate. The most commonly used method for improving the quality of medical image is Contrast enhancement.The main objective is to eliminate the use of contrast dye during the process of MRI scan and to find the parameters MSE, PSNR, AMBE and contrast and compare the result. The histogram equalization (HE) is the widely accepted method which is not productive when the contrast nature differs across the image. Adaptive Histogram Equalization (AHE) overcomes this limitation by considering and developing the mapping for each pixel from the histogram in a neighboring window. Another suitable technique is CLAHE. CLAHE is a refinement of AHE where the enhancement calculation is modified by imposing a user specified level to the height of local histogram. The enhancement is thereby reduced in very uniform areas of the image, which prevents over enhancement of noise and reduces the edge shadowing effect of unlimited AHE. After enhancing the image using AHE and CLAHE the comparison of their parameters is performed.

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MRI brain image enhancement using Histogram Equalization techniques

Cited by 56 publications

References 2 publications

Decision fusion using virtual dictionary‐based sparse representation for robust SAR automatic target recognition

Decision fusion using virtual dictionary‐based sparse representation for robust SAR automatic target recognition

Digital Image Enhancement in Matlab: An Overview on Histogram Equalization and Specification

Contrast Enhancementof MRI Images using AHE and CLAHE Techniques

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