Share Email Print

Proceedings Paper

A representation and classification scheme for tree-like structures in medical images: an application on branching pattern analysis of ductal trees in x-ray galactograms
Author(s): Vasileios Megalooikonomou; Despina Kontos; Joseph Danglemaier; Ailar Javadi; Predrag R. Bakic; Andrew D. A. Maidment
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

We propose a multi-step approach for representing and classifying tree-like structures in medical images. Examples of such tree-like structures are encountered in the bronchial system, the vessel topology and the breast ductal network. We assume that the tree-like structures are already segmented. To avoid the tree isomorphism problem we obtain the breadth-first canonical form of a tree. Our approach is based on employing tree encoding techniques, such as the depth-first string encoding and the Prüfer encoding, to obtain a symbolic representation. Thus, the problem of classifying trees is reduced to string classification where node labels are the string terms. We employ the tf-idf text mining technique to assign a weight of significance to each string term (i.e., tree node label). We perform similarity searches and k-nearest neighbor classification of the trees using the tf-idf weight vectors and the cosine similarity metric. We applied our approach to the breast ductal network manually extracted from clinical x-ray galactograms. The goal was to characterize the ductal tree-like parenchymal structures in order to distinguish among different groups of women. Our best classification accuracy reached up to 90% for certain experimental settings (k=4), outperforming on the average by 10% that of a previous state-of-the-art method based on ramification matrices. These results illustrate the effectiveness of the proposed approach in analyzing tree-like patterns in breast images. Developing such automated tools for the analysis of tree-like structures in medical images can potentially provide insight to the relationship between the topology of branching and function or pathology.

Paper Details

Date Published: 10 March 2006
PDF: 9 pages
Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61441H (10 March 2006); doi: 10.1117/12.651426
Show Author Affiliations
Vasileios Megalooikonomou, Temple Univ. (United States)
Despina Kontos, Temple Univ. (United States)
Joseph Danglemaier, Temple Univ. (United States)
Ailar Javadi, Temple Univ. (United States)
Predrag R. Bakic, Hospital of the Univ. of Pennsylvania (United States)
Andrew D. A. Maidment, Hospital of the Univ. of Pennsylvania (United States)

Published in SPIE Proceedings Vol. 6144:
Medical Imaging 2006: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?