Abstract
Computer Aided Detection (CAD) systems are being developed to assist radiologists in diagnosis. For breast cancer the emphasis is shifting from detection to classification of abnormalities. 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. 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 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. Tree structures used in this study are distinct from decision trees classifiers 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 malignant micro-calcification clusters.
Highlights
Breast cancer is one of the most common diseases found in women [42]
Our project focuses on developing a novel computational model for the classification of malignant and benign calcifications in which we link the clinical aspects of calcifications to a tree structure, which is different from traditional decision tree concepts that have been used in the literature
Each binary tree formed from these 6 nodes has height 0, indicating that all trees belong to the benign class
Summary
Breast cancer is one of the most common diseases found in women [42]. Early detection and assessment can increase the chances of survival [25] and Computer Aided Diagnostic (CAD) systems are being developed to provide a second opinion for diagnosis. Shao et al [40] presented a mathematical model to characterize clustered microcalcifications They graded the micro-calcifications into 4 grades from 0 (benign), 1 (well-differentiated infiltrating ductal carcinoma), 2 (moderately differentiated infiltrating ductal carcinoma) to 3 (poorly differentiated infiltrating ductal carcinoma). Chen et al [11, 12] constructed a micro-calcification graph to represent the topological structure of clusters for the Mammographic Image Analysis Society (MIAS) database [44] They analysed the topological structure by using multiscale morphology and investigated the number of independent subgraphs and the average degree of nodes as feature vectors. Our project focuses on developing a novel computational model for the classification of malignant and benign calcifications in which we link the clinical aspects of calcifications to a tree structure, which is different from traditional decision tree concepts that have been used in the literature.
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