Wavelets for contrast enhancement of digital mammography

Laine, Andrew F.; Fan, Jian; Yang, Wuhai

doi:10.1109/51.464770

Cited by 194 publications

(118 citation statements)

References 32 publications

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“…And test data contains 118 cases of microcalcification and 118 cases of normal. A quantitative measure of contrast improvement is calculated using contrast improvement index (CII) [3]. It is defined by the ratio of enhanced contrast and original contrast.…”

Section: Methodsmentioning

confidence: 99%

“…One method of denoising is wavelet shrinkage that was presented in [3]. We can see that each mammogram contains its own noise characteristics because mammograms are taken from different environment.…”

Section: Fig 3 Homomorphic Filtering Approach For Image Enhancementmentioning

confidence: 99%

“…In mammogram, high attenuation properties and small size of microcalcification are major reasons of poor visualization and small lesions. To improve the visibility of breast cancer, image enhancement methods have been performed [3,4,5,6,7,8]. The aim of these studies was to increase the contrast of microcalcification.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Microcalcification Detection in Computer Aided Diagnosis

Kang¹,

Kim²,

Thạnh³

et al. 2004

Computational Science - ICCS 2004

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Abstract. Microcalcification detection is an important part of early breast cancer detection. In this paper, we propose a microcalcification detection method in mammography CAD (computer-aided diagnosis) system. The proposed microcalcification detection includes two parts. One is adaptive mammogram enhancement algorithm using homomorphic filtering in wavelet. The filter parameters are determined by background characteristics. The other is multi-stage microcalcification detection method. To verify our algorithm, we performed experiments and measured free-response operation characteristics (FROC) curve. The results show that the proposed microcalcification detection method is more robust for fluctuating noisy environments.

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Section: Methodsmentioning

confidence: 99%

Section: Fig 3 Homomorphic Filtering Approach For Image Enhancementmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Microcalcification Detection in Computer Aided Diagnosis

Kang¹,

Kim²,

Thạnh³

et al. 2004

Computational Science - ICCS 2004

View full text Add to dashboard Cite

show abstract

“…The main advantage of using noise removal models is quite high in their speed compared to other techniques. The main drawback of this model is, it does not preserve the edges which are resized in the previous model, and was characterized based on their dissimilarities [20].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Approach to De-noising Mammogram Images

Rajurkar¹,

Singnapure²

2014

International Journal of Advanced Research in Computer and Comm

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Abstract:The noisy nature of digital mammograms and low contrast of doubtful areas which make medical images denoising a challenging problem. In the field of image processing, different types of denoising algorithm are used to remove the unnecessary noise such as additive white Gaussian noise, salt and pepper noise, impulsive noise, as well used to smoothen the image. In the existing work, a novel gradient histogram preservation (GHP) algorithm was developed to improve the texture which exhibits the reduced quality in recognition of human image experiments. To remove the noise, Discrete Wavelet Transform (DWT) algorithm is used to eliminate the noisiness present in the medical image to preserve the texture structures. Proposed approach will efficiently calculate peak signal noise ratio (PSNR) parameters and make the enhanced image without noise and obtains the quality of image without any loss of data.

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“…The wavelet and homomorphic filtering techniques have shown that they are capable of compressing the dynamic range and enhancing the contrast simultaneously [1]- [3]. They concentrate on reinforcing the details of the image to be enhanced in terms of the frequency domain.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Design of Morphology-Based Homomorphic Filter for Feature Enhancement of Medical Images

Hwang¹,

Oh²

2009

International Journal of Fuzzy Logic and Intelligent Systems

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In this paper, a new morphology-based homomorphic filtering technique is presented to enhance features in medical images. The homomorphic filtering is performed based on the morphological sub-bands, in which an image is morphologically decomposed. An evolutionary design is carried to find an optimal gain and structuring element of each sub-band. As a search algorithm, Differential Evolution scheme is utilized. Simulations show that the proposed filter improves the contrast of the interest feature in medical images.

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Wavelets for contrast enhancement of digital mammography

Cited by 194 publications

References 32 publications

Adaptive Microcalcification Detection in Computer Aided Diagnosis

Adaptive Microcalcification Detection in Computer Aided Diagnosis

An Efficient Approach to De-noising Mammogram Images

Evolutionary Design of Morphology-Based Homomorphic Filter for Feature Enhancement of Medical Images

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