A representation and classification scheme for tree-like structures in medical images: analyzing the branching pattern of ductal trees in X-ray galactograms.
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 PrUfer 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.
Megalooikonomou
Vasileios
V
Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122 USA. vasilis@temple.edu
Barnathan
Michael
M
Kontos
Despina
D
Bakic
Predrag R
PR
Maidment
Andrew D A
AD
D000328:Adult; D000368:Aged; D000465:Algorithms; D005260:Female; D006801:Humans; D042361:Mammary Glands, Human / Q000033:anatomy & histology*; D008327:Mammography / Q000379:methods*; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D012372:ROC Curve