Clustered microcalcifications on X-ray mammograms are an important sign for early
detection of breast cancer. Texture-analysis methods can be applied to detect clustered …

We investigated the feasibility of using texture features extracted from mammograms to
predict whether the presence of microcalcifications is associated with malignant or benign …

At present, mammography associated with clinical breast examination and breast self-
examination is the only effective and viable method for mass breast screening. The …

We are developing computerized feature extraction and classification methods to analyze
malignant and benign microcalcifications on digitized mammograms. Morphological features …

Diagnosis of microcalcifications (MCs) is challenged by the presence of dense breast
parenchyma, resulting in low specificity values and thus in unnecessary biopsies. The …

The current study investigates texture properties of the tissue surrounding microcalcification
(MC) clusters on mammograms for breast cancer diagnosis. The case sample analyzed …

Clustered microcalcifcations (MCs) in digitized mammograms has been widely recognized
as an early sign of breast cancer in women. This work is devoted to developing a computer …

A method is described for the automated detection of microcalcifications in digitized
mammograms. The method is based on the Laplacian scale-space representation of the …

We present an evaluation and comparison of the performance of four different texture and
shape feature extraction methods for classification of benign and malignant …

The presence of clusters of microcalcifications is a primary sign of breast cancer. Their
identification is still difficult today for radiologists, and the wrong evaluations involve …