Mammography is a widely used diagnostic technique for early breast cancer detection in women. Clusters of Microcalcification are the sign of breast cancer and their detection will decrease the probability of mortality rate and improves its prognosis. The detection of microcalcification clusters is a difficult task for radiologists because of variations of size and orientation and are highly cor...

PURPOSE To assess the performance of radiologists in the detection of masses and microcalcification clusters on digitized mammograms by using different computer-assisted detection (CAD) cuing environments. MATERIALS AND METHODS Two hundred nine digitized mammograms depicting 57 verified masses and 38 microcalcification clusters in 85 positive and 35 negative cases were interpreted independent...

In mammographic imaging, the presence of microcalcifications, small deposits of calcium in the breast, is a primary indicator of breast cancer. However, not all microcalcifications are malignant and their distribution within the breast can be used to indicate whether clusters of microcalcifications are benign or malignant. Computer-aided diagnosis (CAD) systems can be employed to help classify ...

Continuous wavelet transform is employed to detect singularities in 2-D signals by tracking modulus maxima along maxima lines and particularly applied to microcalcification detection in mammograms. The microcalcifications are modeled as smoothed positive impulse functions. Other target property detection can be performed by adjusting its mathematical model. In this application, the general modu...

PURPOSE The purpose of this study was to assess the breast-specific gamma imaging (BSGI) in Breast Imaging Reporting and Data System (BI-RADS) 4 lesions on mammography and/or ultrasound. METHODS We performed a retrospective review of 162 patients who underwent BSGI in BI-RADS 4 lesions on mammography and/or ultrasound. RESULTS Of the 162 breast lesions, 66 were malignant tumors and 96 were ...

-The proposed method presents a new classification approach to microcalcification detection in mammograms using morphology, Contourlet Transform and Artificial Neural Network. Early detection of breast cancer is possible by enhancing microcalcification features obtained using morphology and singularities of Contourlet Transform. The significant edge information indicating the relevant features ...

In this paper we present a novel approach for microcalcification detection in Digital Breast Tomosynthesis (DBT) datasets. A reconstructionindependent approach, working directly on the projected views, is proposed. Wavelet filter responses on the projections are thresholded and combined to obtain candidate microcalcifications. For each candidate, we create a fuzzy contour through a multi-level ...

The most frequent symptoms of ductal carcinoma recognised by mammography are clusters of microcalcifications. Their detection from mammograms is difficult, especially for glandular breasts. We present a new computer-aided detection system for small field digital mammography in planning of breast biopsy. The system processes the mammograms in several steps. First, we filter the original picture ...

Microcalcifications are deposits of hydroxyapatite (HA) mineral within breast tissue and the most common abnormality detected by mammography when screening for breast cancer due to exhibiting greater X-ray attenuation than the surrounding tissue. However, the detection of microcalcifications is limited by the sensitivity and specificity of mammography. Therefore, the objective of this study was...