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Proceedings Paper

A new framework for detection of initial flat polyp candidates based on a dual level set competition model
Author(s): Huafeng Wang; Lihong C. Li; Xinzhou Wei; Wanquan Liu; Yuehai Wang; Zhengrong Liang
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Paper Abstract

Computer-aided detection (CAD) of colonic polyps plays an important role in advancing computed tomographic colonography (CTC) toward a screening modality. Detection of flat polyps is very challenging because of their plaquelike morphology with limited geometric features for detection purpose. In this paper, we present a novel scheme to automatically detect initial polyp candidates (IPCs) of flat polyp in CTC images. First, tagged materials in CTC images were automatically removed via the partial volume (PV) based electronic colon cleansing (ECC) strategy. We then propose a dual level set competition model to segment the volumetric colon wall from CTC images after ECC. In this model, we developed a comprehensive cost function which takes consideration of the essential characteristics of colon wall such as colon mucosa and weak boundaries, to simulate the mutual interference relationships among those compositions of the colon wall. Furthermore, we introduced a CAD scheme based on the thickness mapping of the colon wall. By tracing the gradient direction of the potential field between inner and outer borders of the colon wall, we focus on the local thickness measures for the detection of IPCs. The proposed CAD approach was validated on patient CTC scans with flat polyps. Experimental results indicate that the present scheme is very promising towards detection of colonic flat polyp candidates via CTC.

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013435 (3 March 2017); doi: 10.1117/12.2254600
Show Author Affiliations
Huafeng Wang, North China Univ. of Technology (China)
Lihong C. Li, City Univ. of New York, College of Staten Island (United States)
Xinzhou Wei, New York City College of Technology (United States)
Wanquan Liu, North China Univ. of Technology (China)
Yuehai Wang, North China Univ. of Technology (China)
Zhengrong Liang, Stony Brook Univ. (United States)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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