described detailed calcification types as typical benign, intermediate concern, and higher probability of malignancy, according to the types and distribution of calcifications described in BI-RADS. described various patterns of breast calcification according to their appearance and distribution from the Americal College of Radiology (ACR) Breast Imaging-Reporting and Data System (BI-RADS) publication. classified common benign lesions as developmental abnormalities, inflammatory lesions, fibrocystic changes, stromal lesions, and neoplasms. used compactness, moments, and Fourier descriptors as a set of shape features for the classification of benign and malignant calcification. They used wavelet decomposition with morphological filtering for the segmentation of candidate calcifications, and then used a connected components approach to identify benign pixels. discussed several types of benign calcification and presented an algorithm for the identification of benign calcifications within the image. Once detected, calcifications can be labelled as benign or malignant, which depends on several properties of the calcifications and incorrect classification can lead to inappropriate or lack of treatment. Early detection and assessment can increase the chances of survival and Computer Aided Diagnostic (CAD) systems are being developed to provide a second opinion for diagnosis. 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 malignant micro-calcification clusters.īreast cancer is one of the most common diseases found in women. Tree structures used in this study are distinct from decision trees classifiers being used in many machine learning approaches. The idea of using tree structure based on the distance of individual calcifications for the classification of benign and malignant micro-calcification clusters is novel and closely related to clinical perception.

After segmentation (automatic or manual), tree-based representations were used to distinguish between benign and malignant clusters, which takes into account clinical criteria such as the number of micro-calcifications in the clusters and their distribution and is based on the topology of the trees and the connectivity of the micro-calcifications. The presented work concentrates on the benign versus malignant classification of micro-calcification clusters, which are a specific type of mammographic abnormality associated with the early development of breast cancer. For breast cancer the emphasis is shifting from detection to classification of abnormalities. Computer Aided Detection (CAD) systems are being developed to assist radiologists in diagnosis.

Breast calcification clusters