Algorithms 2010, 3(1), 1-20; doi:10.3390/a3010001
Article

A Clinical Decision Support Framework for Incremental Polyps Classification in Virtual Colonoscopy

1 Electrical and Computer Engineering Department, American University of Beirut, PO Box 11- 0236, Riad El Solh, Beirut 1107 2020, Lebanon 2 School of Engineering, Virginia Commonwealth University, Richmond, VA, USA 3 Department of Radiology, Harvard Medical School and Massachusetts General Hospital, Boston, MA 02114, USA
* Author to whom correspondence should be addressed.
Received: 28 September 2009; Accepted: 6 October 2009 / Published: 4 January 2010
(This article belongs to the Special Issue Machine Learning for Medical Imaging)
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Abstract: We present in this paper a novel dynamic learning method for classifying polyp candidate detections in Computed Tomographic Colonography (CTC) using an adaptation of the Least Square Support Vector Machine (LS-SVM). The proposed technique, called Weighted Proximal Support Vector Machines (WP-SVM), extends the offline capabilities of the SVM scheme to address practical CTC applications. Incremental data are incorporated in the WP-SVM as a weighted vector space, and the only storage requirements are the hyperplane parameters. WP-SVM performance evaluation based on 169 clinical CTC cases using a 3D computer-aided diagnosis (CAD) scheme for feature reduction comparable favorably with previously published CTC CAD studies that have however involved only binary and offline classification schemes. The experimental results obtained from iteratively applying WP-SVM to improve detection sensitivity demonstrate its viability for incremental learning, thereby motivating further follow on research to address a wider range of true positive subclasses such as pedunculated, sessile, and flat polyps, and over a wider range of false positive subclasses such as folds, stool, and tagged materials.
Keywords: support vector machine; machine learning; medical image analysis; computeraided detection; dynamic multi-classification and unbalanced data sets

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MDPI and ACS Style

Awad, M.; Motai, Y.; Näppi, J.; Yoshida, H. A Clinical Decision Support Framework for Incremental Polyps Classification in Virtual Colonoscopy. Algorithms 2010, 3, 1-20.

AMA Style

Awad M, Motai Y, Näppi J, Yoshida H. A Clinical Decision Support Framework for Incremental Polyps Classification in Virtual Colonoscopy. Algorithms. 2010; 3(1):1-20.

Chicago/Turabian Style

Awad, Mariette; Motai, Yuichi; Näppi, Janne; Yoshida, Hiroyuki. 2010. "A Clinical Decision Support Framework for Incremental Polyps Classification in Virtual Colonoscopy." Algorithms 3, no. 1: 1-20.

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