2009
DOI: 10.1117/1.3099710
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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

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Cited by 8 publications
(5 citation statements)
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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%
See 1 more Smart Citation
“…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%
“…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%
“…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%
“…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