Reconstruction and visualization of 3D models of colonic surface

Hong, Lichan; Zhang, Liang; Viswambharant, A.; Kaufman, Arie; Wax, Mark R.

doi:10.1109/23.597004

Cited by 39 publications

(22 citation statements)

References 18 publications

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“…Computed tomographic colonography (CTC) or CTbased virtual colonoscopy (VC) is an emerging method for the polyp detection. VC utilizes advanced medical imaging and computer technologies to mimic the OC procedure, looking for polyps by navigating through a virtual colon-lumen model which is constructed from the patient abdominal images [3][4][5][6][7][8]. Research findings show that both VC and OC share a similar procedure of high accuracy for detection of clinically significant polyps [9].…”

Section: Introductionmentioning

confidence: 99%

Reduction of false positives by internal features for polyp detection in CT‐based virtual colonoscopy

Wang

Zhang

et al. 2005

Medical Physics

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In this paper, we present a computer-aided detection (CAD) method to extract and use internal features to reduce false positive (FP) rate generated by surface-based measures on the inner colon wall in computed tomographic (CT) colonography. Firstly, a new shape description global curvature, which can provide an overall shape description of the colon wall, is introduced to improve the detection of suspicious patches on the colon wall whose geometrical features are similar to that of the colonic polyps. By a ray-driven edge finder, the volume of each detected patch is extracted as a fitted ellipsoid model. Within the ellipsoid model, CT image density distribution is analyzed. Three types of (geometrical, morphological and textural) internal features are extracted and applied to eliminate the FPs from the detected patches. The presented CAD method was tested by a total of 153 patient datasets in which 45 patients were found with 61 polyps of sizes 4-30 mm by optical colonoscopy. For a 100% detection sensitivity (on polyps), the presented CAD method had an average FPs of 2.68 per patient dataset and eliminated 93.1% of FPs generated by the surface-based measures. The presented CAD method was also evaluated by different polyp sizes. For polyp sizes of 10-30 mm, the method achieved mean number of FPs per dataset of 2.0 with 100% sensitivity. For polyp sizes of 4-10 mm, the method achieved 3.44 FP per dataset with 100% sensitivity.

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Section: Introductionmentioning

confidence: 99%

Reduction of false positives by internal features for polyp detection in CT‐based virtual colonoscopy

Wang

Zhang

et al. 2005

Medical Physics

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“…Several fully automated methods for segmentation of the colon have been developed [2][3][4][5][6][7]. They are roughly divided into two approaches: a surface-generation method for extraction of the inner surface of the colonic wall [2 -5] or extraction of a thick region that encompasses the entire colon [6,7].…”

Section: Segmentation Of the Colonmentioning

confidence: 99%

CAD for CT colonography: current status and future

Yoshida

Dachman

2004

International Congress Series

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“…It has been generally recognized that early removal of the identified colon polyps is the most efficient and successful means to prevent the colorectal cancer [2]. In recent years, the use of three-dimensional (3D) imaging to produce virtual anatomic models of the colon, an emerging technique termed virtual colonoscopy (VC), provides a minimally invasive diagnostic tool for detection of colorectal polyps [3][4][5][6][7][8][9][10][11][12]. However, the major obstacles of VC, such as time-consuming case interpretation time, less diagnosis performance in terms of poor visibility, conspicuity and accuracy of polyp detection, still remain [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Volume-based feature analysis of mucosa for automatic initial polyp detection in virtual colonoscopy

Wang

Zhu

et al. 2008

Int J CARS

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In this paper, we present a volume-based mucosa-based polyp candidate determination scheme for automatic polyp detection in computed colonography. Different from most of the existing computer-aided detection (CAD) methods where mucosa layer is a one-layer surface, a thick mucosa of 3-5 voxels wide fully reflecting partial volume effect is intentionally extracted, which excludes the direct applications of the traditional geometrical features. In order to address this dilemma, fast marching-based adaptive gradient/curvature and weighted integral curvature along normal directions (WICND) are developed for volume-based mucosa. In doing so, polyp candidates are optimally determined by computing and clustering these fast marching-based adaptive geometrical features. By testing on 52 patients datasets in which 26 patients were found with polyps of size 4-22 mm, both the locations and number of polyp candidates detected by WICND and previously developed linear integral curvature (LIC) were compared. The results were promising that WICND outperformed LIC mainly in two aspects: (1) the number of detected false positives was reduced from 706 to 132 on average, which significantly released our burden of machine learning in the feature space, and (2) both the sensitivity and accuracy of polyp detection have been slightly improved, especially for those polyps smaller than 5mm.

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Reconstruction and visualization of 3D models of colonic surface

Cited by 39 publications

References 18 publications

Reduction of false positives by internal features for polyp detection in CT‐based virtual colonoscopy

Reduction of false positives by internal features for polyp detection in CT‐based virtual colonoscopy

CAD for CT colonography: current status and future

Volume-based feature analysis of mucosa for automatic initial polyp detection in virtual colonoscopy

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