Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast-limited adaptive histogram equalization algorithm

Jenifer, S; Parasuraman, S.; Kadirvelu, Amudha

doi:10.1016/j.asoc.2016.01.039

Cited by 114 publications

(44 citation statements)

References 33 publications

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“…(2) Peak signal-to-noise ratio (PSNR) [37][38][39] is commonly used for evaluating image quality and measures how much the enhanced image has degraded when referred to the input image in the area of image enhancement [38]. Here, the formula of PSNR is given in Equation (37).…”

Section: Comparison Of Processed Resultsmentioning

confidence: 99%

Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction

Wan

Qian

et al. 2018

Remote Sensing

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Abstract:Infrared image enhancement is a crucial pre-processing technique in intelligent urban surveillance systems for Smart City applications. Existing grayscale mapping-based algorithms always suffer from over-enhancement of the background, noise amplification, and brightness distortion. To cope with these problems, an infrared image enhancement method based on adaptive histogram partition and brightness correction is proposed. First, the grayscale histogram is adaptively segmented into several sub-histograms by a locally weighted scatter plot smoothing algorithm and local minima examination. Then, the fore-and background sub-histograms are distinguished according to a proposed metric called grayscale density. The foreground sub-histograms are equalized using a local contrast weighted distribution for the purpose of enhancing the local details, while the background sub-histograms maintain the corresponding proportions of the whole dynamic range in order to avoid over-enhancement. Meanwhile, a visual correction factor considering the property of human vision is designed to reduce the effect of noise during the procedure of grayscale re-mapping. Lastly, particle swarm optimization is used to correct the mean brightness of the output by virtue of a reference image. Both qualitative and quantitative evaluations implemented on real infrared images demonstrate the superiority of our method when compared with other conventional methods.

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Section: Comparison Of Processed Resultsmentioning

confidence: 99%

Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction

Wan

Qian

et al. 2018

Remote Sensing

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“…In other words, the intensity values above the threshold is equalized before computing the cumulative density function (CDF) that results in the final image. [16].…”

Section: Methodsmentioning

confidence: 99%

Window Size Effects Using Clahe for Contrast Enhancement in Mammographic Dense Breast Images

Carneiro¹,

Thomaz²,

Patrocinio³

et al. 2018

Anais Do v Congresso Brasileiro De Eletromiografia E Cinesiologia E X Simpósio De Engenharia Biomédica

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Breast cancer is the second most common type of cancer among Brazilian women, globally recognized as a major public health problem. Dense breasts increase the risk of developing breast cancer, mainly due to the difficulty of the lesion visualization, hampering the probability of early detection. In order to enhance the overall quality of mammographic images, digital image processing techniques could improve the visualization of lesions, providing an increase in contrast among breast structures. The goal of this paper is to evaluate the use of the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique for contrast enhancement in mammographic dense breast images. By computing the signal to noise ratio (SNR) and variance, comparing the original images to the processed ones using CLAHE. In this paper, 28 dense breast images were used and separated in mediolateral oblique (MLO) and craniocaudal (CC) views. All the images were processed after the background removal, using CLAHE with seven different window sizes. The results showed that small window sizes decrease the SNR. However, by decreasing window size, an increase in variance was observed, therefore increasing contrast. This increase in contrast was visually observed, on which processed images using small window size performed better enhancement for breast structures, thus resulting in greater contrast. According to these results, we concluded that the CLAHE technique performed better using small window size. In future works, we should apply this algorithm in a greater dense breast image database, as well as preprocessing the images by digital filters for reducing noise.

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“…Breast region is segmented using a threshold. Enhancement techniques such as adaptive histogram equalization, non-linear filtering are applied on the breast region to improve visualization of tissues or a tumor in a mammogram patch (Anand & Gayathri, 2015;Sundaram et al, 2011;Jenifer et al, 2016). In most works, shape features of a mammogram patch have only been considered.…”

Section: Introductionmentioning

confidence: 99%

Density-wise two stage mammogram classification using texture exploiting descriptors

Shastri

Tamrakar

Ahuja

2018

Expert Systems with Applications

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Breast cancer is becoming pervasive with each passing day. Hence, its early detection is a big step in saving the life of any patient. Mammography is a common tool in breast cancer diagnosis. The most important step here is classification of mammogram patches as normal-abnormal and benign-malignant.Texture of a breast in a mammogram patch plays a significant role in these classifications. We propose a variation of Histogram of Gradients (HOG) and Gabor filter combination called Histogram of Oriented Texture (HOT) that exploits this fact. We also revisit the Pass Band -Discrete Cosine Transform (PB-DCT) descriptor that captures texture information well. All features of a mammogram patch may not be useful. Hence, we apply a feature selection technique called Discrimination Potentiality (DP). Our resulting descriptors, DP-HOT and DP-PB-DCT, are compared with the standard descriptors.Density of a mammogram patch is important for classification, and has not been studied exhaustively. The Image Retrieval in Medical Application (IRMA) database from RWTH Aachen, Germany is a standard database that provides mammogram patches, and most researchers have tested their frameworks only on a subset of patches from this database. We apply our two new descriptors on all images of the IRMA database for density wise classification, and compare with the standard descriptors. We achieve higher accuracy than all of the existing standard descriptors (more than 92%).

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Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast-limited adaptive histogram equalization algorithm

Cited by 114 publications

References 33 publications

Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction

Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction

Window Size Effects Using Clahe for Contrast Enhancement in Mammographic Dense Breast Images

Density-wise two stage mammogram classification using texture exploiting descriptors

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