Characterizing colonic detections in CT colonography using curvature-based feature descriptor and bag-of-words model

Authors:
Javed M. Aman;Ronald M. Summers;Jianhua Yao
Affiliations:
Radiology and Imaging Sciences Department, Clinical Center, National Institutes of Health, Bethesda, MD;Radiology and Imaging Sciences Department, Clinical Center, National Institutes of Health, Bethesda, MD;Radiology and Imaging Sciences Department, Clinical Center, National Institutes of Health, Bethesda, MD
Venue:
MICCAI'10 Proceedings of the Second international conference on Virtual Colonoscopy and Abdominal Imaging: computational challenges and clinical opportunities
Year:
2010

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Abstract

We present a method based on the content-based image retrieval (CBIR) paradigm to enhance the performance of computer aided detection (CAD) in computed tomographic colonography (CTC). The method explores curvature-based feature descriptors in conjunction with bag-of-words (BoW) models to characterize colonic detections. The diffusion distance is adopted to improve feature matching and clustering. Word selection is also applied to remove non-informative words. A representative database is constructed to categorize different types of detections. Query detections are compared with the database for classification. We evaluated the performance of the system by using digital phantoms of common structures in the colon as well as real CAD detections. The results demonstrated the potential of our technique for distinguishing common structures within the colon as well as for classifying true and false-positive CAD detections.