Medical Image databases are a key component in future diagnosis and preventive medicine. Automatic categorization of medical images plays an important role for structuring of given medical databases as well as for searching and retrieval of medical images. This paper focuses on a general framework for efficient representation and classification of X-ray images, appropriate for medical image archives. The proposed methodology is comprised of a graph theoretic image representation scheme and image matching measures. In this work, x-ray images are represented by undirected graphs and categorization is done based on an inexact graph matching scheme, graph edit distance. Initially, an unsupervised clustering algorithm is applied on input x-ray images in order to extract coherent regions in feature space, and corresponding coherent segments in the image content. The segmented images are then represented as graphs, which are used in the image matching process. Finally, the experimental results have also been presented at the end of the paper.

Date Published: 26 February 2010
PDF: 7 pages
Proc. SPIE 7546, Second International Conference on Digital Image Processing, 754612 (26 February 2010); doi: 10.1117/12.855091

Published in SPIE Proceedings Vol. 7546:
Second International Conference on Digital Image Processing
Kamaruzaman Jusoff; Yi Xie, Editor(s)