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• W2066883635 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 <i>breadth-first canonical form </i>of a tree. Our approach is based on employing tree encoding techniques, such as the <i>depth-first string encoding</i> and the <i>Prüfer encoding</i>, 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 <i>tf-idf</i> 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 <i>tf-idf</i> 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 (<i>k</i>=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." @default.
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• W2066883635 date "2006-03-02" @default.
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• W2066883635 title "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" @default.
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• W2066883635 doi "https://doi.org/10.1117/12.651426" @default.
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