Medical Images Contrast Enhancement using Quad Weighted Histogram Equalization with Adaptive Gama Correction and Homomorphic Filtering

Agarwal, Monika; Mahajan, Rashima

doi:10.1016/j.procs.2017.09.107

Cited by 32 publications

(22 citation statements)

References 14 publications

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“…During the pixel-level augmentation, the pixel intensities are commonly perturbed using either random or zero-mean Gaussian noise (with the standard deviation corresponding to the appropriate data dimension), with a given probability (the former operation is referred to as the random intensity variation). Other pixel-level operations include shifting and scaling of pixel-intensity values (and modifying the image brightness), applying gamma correction and its multiple variants (Agarwal and Mahajan, 2017;Sahnoun et al, 2018), sharpening, blurring, and more (Galdran et al, 2017). This kind of data augmentation is often exploited for high-dimensional data, as it can be conveniently applied to selected dimensions (Nalepa et al, 2019b).…”

Section: Data Augmentation Using Pixel-level Image Transformationsmentioning

confidence: 99%

Data Augmentation for Brain-Tumor Segmentation: A Review

Nalepa

Marcinkiewicz²,

Kawulok

2019

Front. Comput. Neurosci.

262

127

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Data augmentation is a popular technique which helps improve generalization capabilities of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal role in scenarios in which the amount of high-quality ground-truth data is limited, and acquiring new examples is costly and time-consuming. This is a very common problem in medical image analysis, especially tumor delineation. In this paper, we review the current advances in data-augmentation techniques applied to magnetic resonance images of brain tumors. To better understand the practical aspects of such algorithms, we investigate the papers submitted to the Multimodal Brain Tumor Segmentation Challenge (BraTS 2018 edition), as the BraTS dataset became a standard benchmark for validating existent and emerging brain-tumor detection and segmentation techniques. We verify which data augmentation approaches were exploited and what was their impact on the abilities of underlying supervised learners. Finally, we highlight the most promising research directions to follow in order to synthesize high-quality artificial brain-tumor examples which can boost the generalization abilities of deep models.

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Section: Data Augmentation Using Pixel-level Image Transformationsmentioning

confidence: 99%

Data Augmentation for Brain-Tumor Segmentation: A Review

Nalepa

Marcinkiewicz²,

Kawulok

2019

Front. Comput. Neurosci.

262

127

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“…In this work, intensity normalization techniques applied with MR images have been reviewed. It has been observed that Gaussian filtering [9][10][11][12][13][14] and homomorphic filtering have been mostly used [15][16][17][18][19][20][21][22]. In addition to these methods, there are some deterministic and probabilistic approaches applied with different image modalities in the literature (Section 2).…”

Section: Figure 1 Brain Mr Image and Tissuesmentioning

confidence: 99%

“…Therefore, the σ parameter should be chosen carefully. Another widely used normalization is homomorphic filtering [15][16][17][18][19][20][21][22]. Homomorphic filtering normalizes intensity values by removing multiplicative noise in images.…”

Section: Intensity Normalization Approaches In the Literature: A Surveymentioning

confidence: 99%

Fully Automated and Adaptive Intensity Normalization Using Statistical Features for Brain MR Images

Göçeri¹

2018

Celal Bayar Üniversitesi Fen Bilimleri Dergisi

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Accuracy of the results obtained by automated processing of brain magnetic resonance images has vital importance for diagnosis and evaluation of a progressive disease during treatment. However, automated processing methods such as segmentation, registration and comparison of these images are challenging issues. Because intensity values do not only depend on the underlying tissue type. They can change due to scanner-related artifacts and noise, which usually occurs in magnetic resonance images. In addition to intensity variations, low contrast and partial volume effects increases the difficulty in automated methods with these images. Intensity normalization has a significant role to increase performance of automated image processing methods. Because it is applied as a preprocessing step and efficiency of the other steps in these methods is based on the results obtained from the preprocessing step. The goal of intensity normalization is to make uniform the mean and variance values in images. Different methods have been applied for this purpose in the literature and each method has been tested with different kind of images. In this work; 1) The state-of-art normalization methods applied for magnetic resonance images have been reviewed. 2) A fully automated and adaptive approach has been proposed for intensity normalization in brain magnetic resonance images. 3) Comparative performance evaluations of the results obtained by four different normalization approaches using the same images have been presented. Comparisons of all methods implemented in this work indicate a better performance of the proposed approach for brain magnetic resonance images.

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“…Still there is over enhancement. Monica Agarwal et al [4] devised an efficient algorithm to furnish the limitation of over enhancement with maximum entropy preservation. In the algorithm, input image histogram is segmented based on its valley positions and then weighted distribution is applied to all segmented sub histograms followed by the histogram equalization, gamma correction and homomorphic filtering.…”

Section: Related Workmentioning

confidence: 99%

Contrast Enhancement of Grayscale and Color images using Adaptive Techniques

Mariena¹,

Sathiaseelan²

2018

IJET

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Contrast enhancement is an emerging research area in digital image processing domain. It is an important factor in any subjective evaluation of image quality in medical image processing. As there are possibilities for degradation of image quality during the acquisition, there arises the need of an efficient contrast enhancement technique that can remove the redundant pixels from the images prior to final processing. In this paper, we have proposed two adaptive approaches for contrast enhancement. The first approach is used for enhancing grayscale image using mathematical morphology and second approach is for color image using enhanced sigmoid function. The enhancement process of grayscale image was evaluated by using PSNR and that of color image was evaluated by using a factor called measure of contrast. The experimental results indicate that the two proposed methods show better performance for image in grayscale as well as in color.

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Medical Images Contrast Enhancement using Quad Weighted Histogram Equalization with Adaptive Gama Correction and Homomorphic Filtering

Cited by 32 publications

References 14 publications

Data Augmentation for Brain-Tumor Segmentation: A Review

Data Augmentation for Brain-Tumor Segmentation: A Review

Fully Automated and Adaptive Intensity Normalization Using Statistical Features for Brain MR Images

Contrast Enhancement of Grayscale and Color images using Adaptive Techniques

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