Optimal embedding for shape indexing in medical image databases.
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.
Qian
Xiaoning
X
Dept. of Electrical Engineering, Yale University, New Haven, CT 06520, United States. xiaoning.qian@gmail.com
Tagare
Hemant D
HD
Fulbright
Robert K
RK
Long
Rodney
R
Antani
Sameer
S
D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D002574:Cervical Vertebrae / Q000000981:diagnostic imaging; D003952:Diagnostic Imaging / Q000145:classification*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D008159:Lumbar Vertebrae / Q000000981:diagnostic imaging; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011873:Radiology Information Systems; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013128:Spinal Osteophytosis / Q000000981:diagnostic imaging*