Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Statistics: principles and methods
Statistics: principles and methods
Solid shape
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Intra-patient Prone to Supine Colon Registration for Synchronized Virtual Colonoscopy
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
HDF: Heat diffusion fields for polyp detection in CT colonography
Signal Processing
Extraction Blood Vessels from Retinal Fundus Image Based on Fuzzy C-Median Clustering Algorithm
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Vessel enhancement filter using directional filter bank
Computer Vision and Image Understanding
A probabilistic model for haustral curvatures with applications to colon CAD
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
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Colon cancer is the second major cause of cancer related deaths in industrial nations. Computed tomographic colonography (CTC) has emerged in the last decade as a new less invasive colon diagnostic alternative to the usually practiced optical colonoscopy. The overall goal is to increase the effectiveness of virtual endoscopic navigation of the existing computer-aided detection (CAD) system. The colonic/haustral folds serve as important landmarks for various associated tasks in the virtual endoscopic navigation like prone-supine registration, colonic polyp detection and tenia coli extraction. In this paper, we present two different techniques, first in isolation and then in synergism, for the detection of haustral folds. Our input is volumetric computed tomographic colonography (CTC) images. The first method, which uses a combination of heat diffusion and fuzzy c-means algorithm (FCM), has a tendency of over-segmentation. The second method, which employs level sets, suffers from under-segmentation. A synergistic combination, where the output of the first is used as an input for the second, is shown to improve the segmentation quality. Experimental results are presented on digital colon phantoms as well as real patient scans. The combined method has a total erroneous (over-segmentation plus under-segmentation) detection of (6.5+/-2)% of the total number of folds per colon as compared to (12.5+/-5)% for the diffusion-FCM-based method and (11.5+/-3)% for the level set-based method. The p-values obtained from the associated ANOVA tests indicate that the performance improvements are statistically significant.