Automatic Classification of Linear Structures in Mammographic Images

Zwiggelaar, Reyer, Taylor, Christopher and Boggis, Caroline R. M. (1999) Automatic Classification of Linear Structures in Mammographic Images. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI’99. Lecture Notes in Computer Science, 1679 . Springer Berlin / Heidelberg, pp. 263-270.

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Abstract

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.

Item Type:	Book Section
Faculty \ School:	Faculty of Science > School of Computing Sciences
Depositing User:	Vishal Gautam
Date Deposited:	07 Mar 2011 11:50
Last Modified:	15 Dec 2022 00:53
URI:	https://ueaeprints.uea.ac.uk/id/eprint/23917
DOI:	10.1007/10704282_29

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