Optimal embedding for shape indexing in medical image databases |
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| Authors: | Xiaoning Qian Hemant D Tagare Robert K Fulbright Rodney Long Sameer Antani |
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| Institution: | 1. Dept. of Electrical Engineering, Yale University, New Haven, CT 06520, United States;2. Dept. of Diagnostic Radiology, Yale University, New Haven, CT 06520, United States;3. National Library of Medicine, Bethesda, MD 20894, United States;1. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA;2. University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland;3. National Library of Medicine/National Institutes of Health (NLM/NIH), Bethesda, MD, USA;4. Oregon Health & Science University, Portland, OR, USA;1. Laboratory of Host Defenses, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, USA;2. Department of Medical Education, School of Medicine, Chungnam National University, Daejeon 301-747, Republic of Korea;3. Diabetes Cluster, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA;4. Laboratory of Immunogenetics, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, USA;5. Department of Pathology and Anatomical Sciences, University of Missouri, Sch. of Medicine, Columbia, MO, USA;6. Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA;7. Department of Computer Sciences, University of Missouri, Columbia, MO, USA;8. Department of Pediatrics, University of Colorado Sch. of Medicine, Denver, CO, USA |
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| Abstract: | This paper addresses the problem of indexing shapes in medical image databases. Shapes of organs are often indicative of disease, making shape similarity queries important in medical image databases. Mathematically, shapes with landmarks belong to shape spaces which are curved manifolds with a well defined metric. The challenge in shape indexing is to index data in such curved spaces. One natural indexing scheme is to use metric trees, but metric trees are prone to inefficiency. This paper proposes a more efficient alternative.We show that it is possible to optimally embed finite sets of shapes in shape space into a Euclidean space. After embedding, classical coordinate-based trees can be used for efficient shape retrieval. The embedding proposed in the paper is optimal in the sense that it least distorts the partial Procrustes shape distance.The proposed indexing technique is used to retrieve images by vertebral shape from the NHANES II database of cervical and lumbar spine X-ray images maintained at the National Library of Medicine. Vertebral shape strongly correlates with the presence of osteophytes, and shape similarity retrieval is proposed as a tool for retrieval by osteophyte presence and severity.Experimental results included in the paper evaluate (1) the usefulness of shape similarity as a proxy for osteophytes, (2) the computational and disk access efficiency of the new indexing scheme, (3) the relative performance of indexing with embedding to the performance of indexing without embedding, and (4) the computational cost of indexing using the proposed embedding versus the cost of an alternate embedding. The experimental results clearly show the relevance of shape indexing and the advantage of using the proposed embedding. |
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