27 February 2018 A new fractional order derivative based active contour model for colon wall segmentation
Author Affiliations +
Segmentation of colon wall plays an important role in advancing computed tomographic colonography (CTC) toward a screening modality. Due to the low contrast of CT attenuation around colon wall, accurate segmentation of the boundary of both inner and outer wall is very challenging. In this paper, based on the geodesic active contour model, we develop a new model for colon wall segmentation. First, tagged materials in CTC images were automatically removed via a partial volume (PV) based electronic colon cleansing (ECC) strategy. We then present a new fractional order derivative based active contour model to segment the volumetric colon wall from the cleansed CTC images. In this model, the regionbased Chan-Vese model is incorporated as an energy term to the whole model so that not only edge/gradient information but also region/volume information is taken into account in the segmentation process. Furthermore, a fractional order differentiation derivative energy term is also developed in the new model to preserve the low frequency information and improve the noise immunity of the new segmentation model. The proposed colon wall segmentation approach was validated on 16 patient CTC scans. Experimental results indicate that the present scheme is very promising towards automatically segmenting colon wall, thus facilitating computer aided detection of initial colonic polyp candidates via CTC.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bo Chen, Bo Chen, Lihong C. Li, Lihong C. Li, Huafeng Wang, Huafeng Wang, Xinzhou Wei, Xinzhou Wei, Shan Huang, Shan Huang, Wensheng Chen, Wensheng Chen, Zhengrong Liang, Zhengrong Liang, } "A new fractional order derivative based active contour model for colon wall segmentation", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057517 (27 February 2018); doi: 10.1117/12.2293677; https://doi.org/10.1117/12.2293677

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