A representation and classification scheme for tree-like structures in medical images : Analyzing the branching pattern of ductal trees in x-ray galactograms

Summary, in English

We propose a multistep approach for representing and classifying tree-like structures in medical images. Tree-like structures are frequently encountered in biomedical contexts; examples are the bronchial system, the vascular topology, and the breast ductal network. We use tree encoding techniques, such as the depth-first string encoding and the Prüfer encoding, to obtain a symbolic string representation of the tree's branching topology; the problem of classifying trees is then reduced to string classification. We use the tf-idf text mining technique to assign a weight of significance to each string term (i.e., tree node label). Similarity searches and k-nearest neighbor classification of the trees is performed using the tf-idf weight vectors and the cosine similarity metric. We applied our approach to characterize the ductal tree-like parenchymal structure in X-ray galactograms, in order to distinguish among different radiological findings. Experimental results demonstrate the effectiveness of the proposed approach with classification accuracy reaching up to 86%, and also indicate that our method can potentially aid in providing insight to the relationship between branching patterns and function or pathology.

• Author: Vasileios Megalooikonomou; Michael Barnathan; Despina Kontos; Predrag R. Bakic; Andrew D.A. Maidment
• Publishing year: 2009-04
• Language: English
• Pages: 487-493
• Publication/Series: IEEE Transactions on Medical Imaging
• Volume: 28
• Issue: 4
• Links: http://dx.doi.org/10.1109/TMI.2008.929102
• Document type: Journal article
• Publisher: IEEE - Institute of Electrical and Electronics Engineers Inc.
• Topic: Medical Image Processing
• Keywords: Branching pattern analysis; Characterization; Classification; Tree-like structures; X-ray galactography
• Status: Published
• ISBN/ISSN/Other: ISSN: 0278-0062