Tree-based modelling for the classification of mammographic benign and malignant micro-calcification clusters

Computer Aided Detection (CAD) systems are being developed to
assist radiologists in diagnosis. For breast cancer the emphasis is shifting from
detection to classication of abnormalities. The presented work concentrates
on the benign versus malignant classication of micro-calcication clusters,
which are a specic type of mammographic abnormality associated with the
early development of breast cancer. After segmentation (automatic or man-
ual), tree-based representations were used to distinguish between benign and
malignant clusters, which takes into account clinical criteria such as the num-
ber of micro-calcications in the clusters and their distribution and is based
on the topology of the trees and the connectivity of the micro-calcications.
The idea of using tree structure based on the distance of individual calcica-
tions for the classication of benign and malignant micro-calcication clusters
is novel and closely related to clinical perception. Tree structures used in this
study are distinct from decision trees classiers being used in many machine
learning approaches. Initial evaluation on the Digital Database for Screening
Mammography (DDSM) data shows promising results, with an accuracy equal
to 91%, which is comparable to state of the art CAD systems and is in line
with clinical perception of the morphology and appearance of benign and ma-
lignant micro-calcication clusters.