A new fractional order derivative based active contour model for colon wall segmentation

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.

[1]  Zhengrong Liang,et al.  Increasing computer-aided detection specificity by projection features for CT colonography. , 2010, Medical physics.

[2]  Zhengrong Liang,et al.  Reduction of false positives by internal features for polyp detection in CT-based virtual colonoscopy. , 2005, Medical physics.

[3]  Zhengrong Liang,et al.  Virtual colonoscopy vs optical colonoscopy. , 2010, Expert opinion on medical diagnostics.

[4]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[5]  Zhengrong Liang,et al.  Texture Feature Extraction and Analysis for Polyp Differentiation via Computed Tomography Colonography , 2016, IEEE Transactions on Medical Imaging.

[6]  Zhengrong Liang,et al.  Volumetric texture features from higher-order images for diagnosis of colon lesions via CT colonography , 2014, International Journal of Computer Assisted Radiology and Surgery.

[7]  Pu Yi Fractional Differential Masks of Digital Image and Their Numerical Implementation Algorithms , 2007 .

[8]  Zhengrong Liang,et al.  Improved curvature estimation for computer-aided detection of colonic polyps in CT colonography. , 2011, Academic radiology.

[9]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[10]  Zhengrong Liang,et al.  Detection of colonic polyp candidates with level set-based thickness mapping over the colon wall , 2015, Medical Imaging.

[11]  Zhengrong Liang,et al.  Integration of 3D scale-based pseudo-enhancement correction and partial volume image segmentation for improving electronic colon cleansing in CT colonograpy. , 2014, Journal of X-ray science and technology.

[12]  Zhengrong Liang,et al.  3D virtual colonoscopy , 1995, Proceedings 1995 Biomedical Visualization.

[13]  A. Jemal,et al.  Cancer statistics, 2017 , 2017, CA: a cancer journal for clinicians.

[14]  Zhengrong Liang,et al.  A Novel Minimal Surface Overlay Model for the Whole Colon Wall Segmentation , 2014, ABDI@MICCAI.

[15]  Zemin Ren,et al.  Adaptive active contour model driven by fractional order fitting energy , 2015, Signal Process..

[16]  C. G. Coin,et al.  Computerized radiology of the colon: a potential screening technique. , 1983, Computerized radiology : official journal of the Computerized Tomography Society.

[17]  I. Bitter,et al.  Detection of Colon Wall Outer Boundary and Segmentation of the Colon Wall Based on Level Set Methods , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  Zhengrong Liang,et al.  An EM approach to MAP solution of segmenting tissue mixture percentages with application to CT-based virtual colonoscopy. , 2008, Medical physics.

[19]  Zhengrong Liang,et al.  ROC operating point selection for classification of imbalanced data with application to computer-aided polyp detection in CT colonography , 2013, International Journal of Computer Assisted Radiology and Surgery.

[20]  Zhengrong Liang,et al.  A Novel Dual LevelSets Competition Model for Colon Region Segmentation , 2015, CARE@MICCAI.