Detection and Modelling of the Distribution of Linear Structure in Mammographic Images

Abstract

Mammographic risk assessment is concerned with estimating the probability of a woman developing breast cancer. The aim is to improve the likelihood of early detection of breast cancers. The leading factor in determining risk is breast density, which has been shown to be the most accurate measure of mammographic risk, however more recently it has been suggested that the density (and possibly the distribution) of linear structures such as ducts and blood vessels within the breast are also related to mammographic risk. The purpose of this project is to investigate those relationships and the possibility of including this information in an automated risk assessment system. A methodology is developed for detecting the linear structures in 2D ammograms. This information is used to calculate the density of linear structures which is used in a risk classifier. Results show that a classifier based on the density of linear structures outperforms a classifier based on breast density (64% correct BIRADS classification using linear density compared with 53% using breast density), and that a classifier combining both factors outperforms both individual classifiers (74% correct BIRADS classification), suggesting that linear density is related to risk and provides useful information for risk assessment. The investigation in to the distribution of linear structures focusses on 3D tomosynthesis images. The linear structure detection methodology is developed for use in 3D, and a graph representation of the linear structures is extracted. Information from this graph relating to the distribution of linear structures is used for classification. The results of this classification (79% correct BIRADS classification) suggest that the distribution of linear structures is also related to risk and that this information provides additional risk–related information useful for risk assessment

Date of Award	15 Apr 2013
Original language	English
Awarding Institution	Aberystwyth University
Supervisor	Reyer Zwiggelaar (Supervisor) & Yonghuai Liu (Supervisor)