摘要：Breast cancer is a major cause of death and morbidity among women
all over the world, and it is a fact that early detection is a key
in improving outcomes. Therefore development of algorithms that
aids radiologists in identifying changes in breast tissue early on
is essential. In this work an algorithm that investigates the use
of principal components analysis (PCA) is developed to identify
suspicious regions on mammograms. The algorithm employs linear
structure and curvelinear modeling prior to PCA implementations.
Evaluation of the algorithm is based on the percentage of correct
classification, false positive (FP) and false negative (FN) in all
experimental work using real data. Over 90% accuracy in block
classification is achieved using mammograms from MIAS database.
