Paper
27 February 2009 Two methods of Haustral fold detection from computed tomographic virtual colonoscopy images
Author Affiliations +
Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72602U (2009) https://doi.org/10.1117/12.811031
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Virtual colonoscopy (VC) has gained popularity as a new colon diagnostic method over the last decade. VC is a new, less invasive alternative to the usually practiced optical colonoscopy for colorectal polyp and cancer screening, the second major cause of cancer related deaths in industrial nations. Haustral (colonic) folds serve as important landmarks for virtual endoscopic navigation in the existing computer-aided-diagnosis (CAD) system. In this paper, we propose and compare two different methods of haustral fold detection from volumetric computed tomographic virtual colonoscopy images. The colon lumen is segmented from the input using modified region growing and fuzzy connectedness. The first method for fold detection uses a level set that evolves on a mesh representation of the colon surface. The colon surface is obtained from the segmented colon lumen using the Marching Cubes algorithm. The second method for fold detection, based on a combination of heat diffusion and fuzzy c-means algorithm, is employed on the segmented colon volume. Folds obtained on the colon volume using this method are then transferred to the corresponding colon surface. After experimentation with different datasets, results are found to be promising. The results also demonstrate that the first method has a tendency of slight under-segmentation while the second method tends to slightly over-segment the folds.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ananda S. Chowdhury, Sovira Tan, Jianhua Yao, Marius G. Linguraru, and Ronald M. Summers "Two methods of Haustral fold detection from computed tomographic virtual colonoscopy images", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72602U (27 February 2009); https://doi.org/10.1117/12.811031
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Cited by 3 scholarly publications.
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KEYWORDS
Colon

Diffusion

Virtual colonoscopy

Computer aided diagnosis and therapy

Fuzzy logic

Tomography

Image segmentation

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