Detection of microcalcifications in mammograms using error of prediction and statistical measures

Acha, Begoña; Serrano, Carmen; Rangayyan, Rangaraj M.; Desautels, J. E. Leo

doi:10.1117/1.3099710

Cited by 8 publications

(5 citation statements)

References 58 publications

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“…Among the lesions evaluated in mammographic reading, special attention is given to clustered microcalcifications because this arrangement is often associated with malignant tumors [10][11][12]. Due to their small size and mammographic image noise, the contrast between microcalcifications and breast tissue is relatively poor [11].…”

Section: Introductionmentioning

confidence: 99%

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Mammographic Image Denoising and Enhancement Using the Anscombe Transformation, Adaptive Wiener Filtering, and the Modulation Transfer Function

et al. 2012

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A new restoration methodology is proposed to enhance mammographic images through the improvement of contrast features and the simultaneous suppression of noise. Denoising is performed in the first step using the Anscombe transformation to convert the signal-dependent quantum noise into an approximately signal-independent Gaussian additive noise. In the Anscombe domain, noise is filtered through an adaptive Wiener filter, whose parameters are obtained by considering local image statistics. In the second step, a filter based on the modulation transfer function of the imaging system in the whole radiation field is applied for image enhancement. This methodology can be used as a preprocessing module for computer-aided detection (CAD) systems to improve the performance of breast cancer screening. A preliminary assessment of the restoration algorithm was performed using synthetic images with different levels of quantum noise. Afterward, we evaluated the effect of the preprocessing on the performance of a previously developed CAD system for clustered microcalcification detection in mammographic images. The results from the synthetic images showed an increase of up to 11.5 dB (p = 0.002) in the peak signal-to-noise ratio. Moreover, the mean structural similarity index increased up to 8.3 % (p < 0.001). Regarding CAD performance, the results suggested that the preprocessing increased the detectability of microcalcifications in mammographic images without increasing the false-positive rates. Receiver operating characteristic analysis revealed an average increase of 14.1 % (p = 0.01) in overall CAD performance when restored image sets were used.

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Section: Introductionmentioning

confidence: 99%

“…Due to their small size and mammographic image noise, the contrast between microcalcifications and breast tissue is relatively poor [11]. Microcalcifications may be inconspicuous and may be missed, even by a diligent radiologist [12]. The problem is worsened by dense-breast tissue.…”

Section: Introductionmentioning

confidence: 99%

Mammographic Image Denoising and Enhancement Using the Anscombe Transformation, Adaptive Wiener Filtering, and the Modulation Transfer Function

et al. 2012

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“…These potential seeds are subsequently validated by calculating for each the statistical tail ratio that provides information about the distribution of intensities in the histogram of a window centered on the pixel being studied. 8 In each selected seed a region growth is applied in which neighboring pixels are added having an intensity above the mean value plus the typical deviation of a window centered on the seed having a size of 10 × 10 pixels. For the growing region to be valid it must not have a surface exceeding 30 pixels because in our database and at the resolution we work with there is no microaneurysm having an extension above said value (Fig.…”

Section: Preprocessing Seed Selection Rregion Growth Classificatgionmentioning

confidence: 99%

Automated detection of microaneurysms by using region growing and fuzzy artmap neural network

Jiménez

Alemany

Núñez

et al. 2012

Archivos de la Sociedad Española de Oftalmología (English Editi

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Objective: To assess whether the methodological changes of this new algorithm improves the results of a previously presented strategy.Methods: We enhance the image and filter out the green channel of the digital color retinography. Multitolerance thresholding was applied to obtain candidate points and make a seed growing region by varying intensities. We took 15 characteristics from each region to train a fuzzy Artmap neural network using 42 retinal photographs. This network was then applied in the study of 11 good quality retinal photographs included in the diabetic retinopathy early detection screening program, with initial stages of retinopathy, obtained with the Topcon NW200 non-mydriatic retinal camera.Results: Two experienced ophthalmologists detected 52 microaneurysms in 11 images. The algorithm detected 39 microaneurysms and 3752 more regions, confirming 38 microaneurysm and 135 false positives. The sensitivity is improved compared to the previous algorithm, from 60.53% to 73.08%. False positives have dropped from 41.8 to 12.27 per image. Conclusions:The new algorithm is better than the previous one, but there is still room for improvement, especially in the initial determination of seeds.

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“…Al umbralizar se marca el píxel de mayor intensidad de cada región segmentada como candidato a semilla. Estos candidatos a semilla son validados posteriormente mediante el cálculo -en cada uno-del estadístico tail ratioque nos aporta información acerca de la distribución de las intensidades en el histograma de una ventana centrada en el píxel bajo estudio 8 .…”

Section: Crecimiento De Regiones Clasificaciónunclassified

Detección automatizada de microaneurismas mediante crecimiento de regiones y red neuronal Fuzzy Artmap

Jiménez

Alemany

Núñez

et al. 2012

Archivos de la Sociedad Española de Oftalmología

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Detection of microcalcifications in mammograms using error of prediction and statistical measures

Abstract: Abstract. A two-stage method for detecting microcalcifications in mammograms is

Cited by 8 publications

References 58 publications

Mammographic Image Denoising and Enhancement Using the Anscombe Transformation, Adaptive Wiener Filtering, and the Modulation Transfer Function

Mammographic Image Denoising and Enhancement Using the Anscombe Transformation, Adaptive Wiener Filtering, and the Modulation Transfer Function

Automated detection of microaneurysms by using region growing and fuzzy artmap neural network

Detección automatizada de microaneurismas mediante crecimiento de regiones y red neuronal Fuzzy Artmap

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