Maria V. Sainz de Cea

  • Citations Per Year
Learn More
Due to variability among different subjects, the detection accuracy of microcalcifications (MC) in mammograms often varies greatly from case to case. Even for a well-developed MC detector, its performance can be hampered by a number of factors ranging from imaging noise to inhomogeneity in the breast tissue. To address this issue, we use a Bayes' risk(More)
Computerized detection of clustered microcalcifications (MCs) in mammograms often suffers from the occurrence of false positives (FPs), which can vary greatly from case to case. We investigate how to apply statistical estimation to determine the number of FPs that are present in a detected MC lesion. First, we describe the number of true positives (TPs) by(More)
In computerized detection of clustered microcalcifications (MCs) from mammogram images the occurrence of false positives (FPs) varies greatly from case to case. In this work, we develop a probabilistic modeling approach to estimate the number of individual FPs present in a detected MC lesion. We describe the number of true positives (TPs) by a(More)
In computer-aided diagnosis of clustered microcalcifications (MCs), the individual MCs in a lesion need to be first detected prior to subsequent classification as being benign or malignant. However, owing to noise characteristics and patient variability, the detection accuracy is often adversely compromised by the occurrence of false-positives (FPs) or(More)
  • 1