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

Vasileios Megalooikonomou; Despina Kontos; Joseph Danglemaier; Ailar Javadi; Predrag R. Bakic; Andrew D. A. Maidment

doi:10.1117/12.651426

10 March 2006 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

Vasileios Megalooikonomou, Despina Kontos, Joseph Danglemaier, Ailar Javadi, Predrag R. Bakic, Andrew D. A. Maidment

Author Affiliations +

Proceedings Volume 6144, Medical Imaging 2006: Image Processing; 61441H (2006) https://doi.org/10.1117/12.651426
Event: Medical Imaging, 2006, San Diego, California, United States

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.

Citation Download Citation

Vasileios Megalooikonomou, Despina Kontos, Joseph Danglemaier, Ailar Javadi, Predrag R. Bakic, and Andrew D. A. Maidment "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", Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61441H (10 March 2006); https://doi.org/10.1117/12.651426

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available