Detection of calcifications in digital mammograms using wavelet analysis and contrast enhancement

Salvadó, José A.; Roque, Bruno

doi:10.1109/wisp.2005.1531658

Cited by 24 publications

(11 citation statements)

References 10 publications

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“…Finally, the third group includes multiscale approaches by wavelet transform, 25,17,38,42 B-spline function, 44 decimated wavelet transform, 48,49 undecimated biorthogonal transform, 43,45 fractal 28,27 and morphological analysis. 47 The latter two methods are used especially in strongly low contrast images, where the dense tissue reduces the visibility of microcalcifications.…”

Section: Introduction and Statement Of Main Resultsmentioning

confidence: 99%

Denoising and Enhancement of Mammographic Images Under the Assumption of Heteroscedastic Additive Noise by an Optimal Subband Thresholding

Mencattini

Rabottino

Salmeri

et al. 2010

Int. J. Wavelets Multiresolut Inf. Process.

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Mammographic images suffer from low contrast and signal dependent noise, and a very small size of tumoral signs is not easily detected, especially for an early diagnosis of breast cancer. In this context, many methods proposed in literature fail for lack of generality. In particular, too weak assumptions on the noise model, e.g., stationary normal additive noise, and an inaccurate choice of the wavelet family that is applied, can lead to an information loss, noise emphasizing, unacceptable enhancement results, or in turn an unwanted distortion of the original image aspect. In this paper, we consider an optimal wavelet thresholding, in the context of Discrete Dyadic Wavelet Transforms, by directly relating all the parameters involved in both denoising and contrast enhancement to signal dependent noise variance (estimated by a robust algorithm) and to the size of cancer signs. Moreover, by performing a reconstruction from a zero-approximation in conjunction with a Gaussian smoothing filter, we are able to extract the background and the foreground of the image separately, as to compute suitable contrast improvement indexes. The whole procedure will be tested on high resolution X-ray mammographic images and compared with other techniques. Anyway, the visual assessment of the results by an expert radiologist will be also considered as a subjective evaluation.

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Section: Introduction and Statement Of Main Resultsmentioning

confidence: 99%

Denoising and Enhancement of Mammographic Images Under the Assumption of Heteroscedastic Additive Noise by an Optimal Subband Thresholding

Mencattini

Rabottino

Salmeri

et al. 2010

Int. J. Wavelets Multiresolut Inf. Process.

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show abstract

“…Due to this small size mcirocalcifications are very difficult to detect. In addition the distribution and shapes of the microcalcifications vary widely [5,6] as shown in Figure 5. The low contrast nature of these microcalcifications with respect to their surrounding tissues causes problem for the radiologists to detect them.…”

Section: Figure 5 Mammogram Images With Calcificationsmentioning

confidence: 99%

Fourier Transform Based Early Detection of Breast Cancer by Mammogram Image Processing

Tarique

ElZahra

Hateem

et al. 2015

JBEMi

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Breast cancer is very common among the World's women population. Early detection of breast cancer can save life. Mammography based diagnosis is considered as the most effective to detect breast cancer. But, mammogram images often lead to misdiagnosis due to their low contrast nature. Recent studies show that mammography based diagnosis fails to detect cancerous lumps in the breast in ten out of one hundred patients. In this paper we address this issue. We propose a computer aided breast cancer detection technique in this paper. In the proposed method mammogram images are enhanced and segmented to locate the malignant lumps in the women's breast. The segmented image is then compared with a template image in order to determine the stage of breast cancer. Some statistical characteristics have also been presented in this paper to identify the malignant lumps from the benign ones.

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“…Numerous image enhancement methods have been published for preventive mammography screening [3,5,6,8,12,15,18,19,22,24,26,29]. Around 25% of radiologically visible cancers are missed by the radiologists at screening [23] which means that millions of cancer cases are missed every year.…”

Section: Introductionmentioning

confidence: 99%

Region of interest contrast measures

Reměs¹,

Haindl²

2018

Kybernetika

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Detection of calcifications in digital mammograms using wavelet analysis and contrast enhancement

Cited by 24 publications

References 10 publications

Denoising and Enhancement of Mammographic Images Under the Assumption of Heteroscedastic Additive Noise by an Optimal Subband Thresholding

Denoising and Enhancement of Mammographic Images Under the Assumption of Heteroscedastic Additive Noise by an Optimal Subband Thresholding

Fourier Transform Based Early Detection of Breast Cancer by Mammogram Image Processing

Region of interest contrast measures

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