This paper presents a multiresolution approach to the computer aided detection of clustered microcalcifications in digitized mammograms based on Gabor elementary functions. A bank of Gabor functions with varying spatial extent and tuned to different spatial frequencies is used for the extraction of microcalcifications characteristics. Classification is performed by an Artificial Neural Network with supervised learning.

First results show that most microcalcifications, isolated or clustered, are detected by our algorithm with a 95% value both for sensibility and specificity as measured on a test data set.