Investigation of clustered microcalcification features for an automated classifier as part of a mammography CAD scheme

Abstract

Classification of breast microcalcifications and clusters depends characteristics selected to be the input for an automated classifier. Artificial neural networks have been used to aid in classification of structures on mammograms images. However, to achieve the classification, some attributes have to be adequately extracted from the images in the database used for tests. As a part of a CAD scheme in development, our intention is to establish a ANN-based classifier, intended to distribute detected clustered microcalcifications in one of 5 classes, according to BI-RADS classification (normal, benign, probably benign, suspicious and probably malignant). This work reports a part of this procedure, by extracting and selecting most of significant characteristics regarding digitized mammography images containing clustered microcalcifications. Two distinct classes-probably benign and suspicious-are considered in order to compare the selected characteristics incidence distribution. Distance between both classes could be estimated by using Gaussian curves. Images used for the tests were from a database composed by mammograms digitized with 600 dpi of spatial resolution in a andbit grayscale. The regions of interest were selected based on physicians reports on the existence of a cluster. This study has shown that characteristics just as irregularity, number of microcalcifications in a cluster, and cluster area are already enough to separate the processed images in two very distinct classes-suspicious and probably benign, although other features could be necessary for a more detailed classification.