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Proceedings Paper

A contour-based shape descriptor for biomedical image classification and retrieval
Author(s): Daekeun You; Sameer Antani; Dina Demner-Fushman; George R. Thoma
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Paper Abstract

Contours, object blobs, and specific feature points are utilized to represent object shapes and extract shape descriptors that can then be used for object detection or image classification. In this research we develop a shape descriptor for biomedical image type (or, modality) classification. We adapt a feature extraction method used in optical character recognition (OCR) for character shape representation, and apply various image preprocessing methods to successfully adapt the method to our application. The proposed shape descriptor is applied to radiology images (e.g., MRI, CT, ultrasound, X-ray, etc.) to assess its usefulness for modality classification. In our experiment we compare our method with other visual descriptors such as CEDD, CLD, Tamura, and PHOG that extract color, texture, or shape information from images. The proposed method achieved the highest classification accuracy of 74.1% among all other individual descriptors in the test, and when combined with CSD (color structure descriptor) showed better performance (78.9%) than using the shape descriptor alone.

Paper Details

Date Published: 24 March 2014
PDF: 12 pages
Proc. SPIE 9021, Document Recognition and Retrieval XXI, 90210L (24 March 2014); doi: 10.1117/12.2042526
Show Author Affiliations
Daekeun You, National Institutes of Health (United States)
Sameer Antani, National Institutes of Health (United States)
Dina Demner-Fushman, National Institutes of Health (United States)
George R. Thoma, National Institutes of Health (United States)


Published in SPIE Proceedings Vol. 9021:
Document Recognition and Retrieval XXI
Bertrand Coüasnon; Eric K. Ringger, Editor(s)

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