Automatic Classification of Linear Structures in Mammographic Images

Reyer Zwiggelaar, Christopher Taylor, Caroline R. M. Boggis

Research output: Chapter in Book/Report/Conference proceedingChapter


Certain kinds of abnormalities in x-ray mammograms are associated with specific anatomical structures – in particular, linear structures. This association can, in principle, be exploited to improve the specificity and sensitivity with which the abnormalities can be detected. We compare annotated and the automatic detection of the scale and orientation associated with linear structure in mammograms. We investigate methods of classifying the detected structures into anatomical classes (spicules, vessel, duct, fibrous tissue etc) from their cross-sectional profiles. Automatic (linear and non-linear) classification results are compared with expert annotations using receiver operating characteristic analysis. We show that useful discrimination between anatomical classes is achieved. Some of this relies on simple attributes such as the width and contrast of the profile, but there is also important information carried by the shape of the profile.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention – MICCAI’99
EditorsChris Taylor, Alain Colchester
PublisherSpringer Berlin / Heidelberg
Number of pages8
Publication statusPublished - 1999

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin / Heidelberg

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