Title
A new framework for detection of initial flat polyp candidates based on a dual level set competition model.
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.
Year
DOI
Venue
2017
10.1117/12.2254600
Proceedings of SPIE
Keywords
Field
DocType
CT colonography,colonic polyps,colon wall,wall thickness,level set,computer-aided detection
CAD,Computer vision,Colon cleansing,Gradient direction,Computer-aided diagnosis,Level set,Artificial intelligence,Colon wall,Virtual colonoscopy,Partial volume,Physics
Conference
Volume
ISSN
Citations 
10134
0277-786X
0
PageRank 
References 
Authors
0.34
1
6
Name
Order
Citations
PageRank
Huafeng Wang1597.87
Lihong C. Li200.68
xinzhou wei301.69
Wanquan Liu462981.29
Yuehai Wang501.35
Zhengrong Liang668493.03