Hierarchical feature clustering for content-based retrieval in medical image databases

Christian Thies; Adam Malik; Daniel Keysers; Michael Kohnen; Benedikt Fischer; Thomas Martin Lehmann

doi:10.1117/12.481393

15 May 2003 Hierarchical feature clustering for content-based retrieval in medical image databases

Christian Thies, Adam Malik, Daniel Keysers, Michael Kohnen, Benedikt Fischer, Thomas Martin Lehmann

Proceedings Volume 5032, Medical Imaging 2003: Image Processing; (2003) https://doi.org/10.1117/12.481393
Event: Medical Imaging 2003, 2003, San Diego, California, United States

Abstract

In this paper we describe the construction of hierarchical feature clustering and show how to overcome general problems of region growing algorithms such as seed point selection and processing order. Access to medical knowledge inherent in medical image databases requires content-based descriptions to allow non-textual retrieval, e.g., for comparison, statistical inquiries, or education. Due to varying medical context and questions, data structures for image description must provide all visually perceivable regions and their topological relationships, which poses one of the major problems for content extraction. In medical applications main criteria for segmenting images are local features such as texture, shape, intensity extrema, or gray values. For this new approach, these features are computed pixel-based and neighboring pixels are merged if the Euclidean distance of corresponding feature vectors is below a threshold. Thus, the planar adjacency of clusters representing connected image partitions is preserved. A cluster hierarchy is obtained by iterating and recording the adjacency merging. The resulting inclusion and neighborhood relations of the regions form a hierarchical region adjacency graph. This graph represents a multiscale image decomposition and therefore an extensive content description. It is examined with respect to application in daily routine by testing invariance against transformation, run time behavior, and visual quality For retrieval purposes, a graph can be matched with graphs of other images, where the quality of the matching describes the similarity of the images.

Citation Download Citation

Christian Thies, Adam Malik, Daniel Keysers, Michael Kohnen, Benedikt Fischer, and Thomas Martin Lehmann "Hierarchical feature clustering for content-based retrieval in medical image databases", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.481393

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