Digital mammogram enhancement based on automatic histogram clipping

Joda, Bubakari; Dereboylu, Ziya

doi:10.1109/cicn.2017.8319351

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…In women, breast cancer is the most common disease after lung cancer [1,2]. Its detection and treatment in the early phases enhance the chance of successful recovery from the disease.…”

Section: Introductionmentioning

confidence: 99%

Contrast Enhancement in Mammograms Using Convolution Neural Networks for Edge Computing Systems

Hashmi¹,

Juneja²,

Kumar³

et al. 2022

Scientific Programming

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A good contrast is significant for analysis of medical images, and if the images have poor contrast, then some methods of contrast enhancement can be of much benefit. In this paper, a convolution neural network-based transfer learning approach is utilized for contrast enhancement of mammographic images. The experiments are conducted on ISP and MIAS datasets, where ISP dataset is used for training and MIAS dataset is used for testing (contrast enhancement). Experimental comparison of the proposed technique is done with the most popular direct and indirect contrast enhancement techniques such as CLAHE, BBHE, RMSHE, and contrast stretching. A qualitative comparison is done using mean square error (MSE), signal to noise ratio (SNR), and peak signal to noise ratio (PSNR). It is observed that the proposed technique outperforms the other techniques HE, RMSHE, CLAHE, BBHE, and contrast stretching.

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“…In women, breast cancer is the most common disease after lung cancer [1,2]. Its detection and treatment in the early phases enhance the chance of successful recovery from the disease.…”

Section: Introductionmentioning

confidence: 99%

Contrast Enhancement in Mammograms Using Convolution Neural Networks for Edge Computing Systems

Hashmi¹,

Juneja²,

Kumar³

et al. 2022

Scientific Programming

View full text Add to dashboard Cite

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Enhancing mammography: a comprehensive review of computer methods for improving image quality

Santos,

Abreu

2024

Prog. Biomed. Eng.

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Mammography imaging remains the gold standard for breast cancer detection and diagnosis, but challenges in image quality can lead to misdiagnosis, increased radiation exposure, and higher healthcare costs. This comprehensive review evaluates traditional and machine learning-based techniques for improving mammography image quality, aiming to benefit clinicians and enhance diagnostic accuracy. Our literature search, spanning 2015 – 2024, identified 115 articles focusing on contrast enhancement and noise reduction methods, including histogram equalization, filtering, unsharp masking, fuzzy logic, transform-based techniques, and advanced machine learning approaches. Machine learning, particularly architectures integrating denoising autoencoders with convolutional neural networks, emerged as highly effective in enhancing image quality without compromising detail. The discussion highlights the success of these techniques in improving mammography images’ visual quality. However, challenges such as high noise ratios, inconsistent evaluation metrics, and limited open-source datasets persist. Addressing these issues offers opportunities for future research to further advance mammography image enhancement methodologies.

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