Colonic polyp characterization and detection based on both morphological and texture features

Wang, Zigang; Li, Lihong; Anderson, Joseph C.; Harrington, Donald P.; Zhang, Liang

doi:10.1016/j.ics.2004.03.146

Cited by 9 publications

(6 citation statements)

References 14 publications

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“…The experimental data indicates that our polyp detection technique also outperforms the methods reported in [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] especially when dealing with small and medium sized polyps. Also it is worth mentioning that our algorithm exhibits a remarkable robustness to noise.…”

Section: Discussionmentioning

confidence: 76%

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The use of 3D surface fitting for robust polyp detection and classification in CT colonography

Chowdhury¹,

Whelan²,

Ghita³

2006

Computerized Medical Imaging and Graphics

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In this paper we describe the development of a computationally efficient computeraided detection (CAD) algorithm based on the evaluation of the surface morphology that is employed for the detection of colonic polyps in computed tomography (CT) colonography. Initial polyp candidate voxels were detected using the surface normal intersection values. These candidate voxels were clustered using the normal direction, convexity test, region growing and Gaussian distribution. The local colonic surface was classified as polyp or fold using a feature normalized nearest neighborhood classifier. The main merit of this paper is the methodology applied to select the robust features derived from the colon surface that have a high discriminative power for polyp/fold classification. The devised polyp detection scheme entails a low computational overhead (typically takes 2.20 minute per dataset) and shows 100% sensitivity for phantom polyps greater than 5mm. It also shows 100% sensitivity for Preprint submitted to Elsevier Science 19 May 2006real polyps larger than 10mm and 91.67% sensitivity for polyps between 5 to 10mmwith an average of 4.5 false positives per dataset. The experimental data indicates that the proposed CAD polyp detection scheme outperforms other techniques that identify the polyps using features that sample the colon surface curvature especially when applied to low-dose datasets.

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Section: Discussionmentioning

confidence: 76%

“…Other interesting automated CAD-CTC techniques include the work of Gokturk et al [25], Acar et al [26], Wanga at el. [27], Jerebko et al [28] and Kiraly et al [29].…”

Section: Introductionmentioning

confidence: 98%

The use of 3D surface fitting for robust polyp detection and classification in CT colonography

Chowdhury¹,

Whelan²,

Ghita³

2006

Computerized Medical Imaging and Graphics

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“…Approximately 10% of the false positives were generated by the residual material attached to the colonic surface. These false positives can be eliminated by texture analysis [19] as they have a different density than folds and polyps. By using surface normal intersection and statistical features calculated from polyps/folds morphology and least square fitting, we aimed to extract the optimal features that capture the surface convexity.…”

Section: A Discussion and Conclusionmentioning

confidence: 99%

A statistical approach for robust polyp detection in CT colonography

Chowdhury

Ghita

Whelan

2005

2005 IEEE Engineering in Medicine and Biology 27th Annual Conference

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Abstract-In this paper we describe the development of a computationally efficient computer-aided detection (CAD) algorithm based on the statistical features derived from the local colonic surface that are used for the detection of colonic polyps in computed tomography (CT) colonography. The candidate surface voxels were detected and clustered using the surface normal intersection, convexity test, region growing and Hough Transform. The main objective of this paper is the selection of the statistical features that optimally capture the convexity of the candidate surface and consequently provide a high discrimination between local surfaces defined by polyps and folds. The developed polyp detection scheme is computationally efficient (typically takes 3.9 minute per dataset) and shows 100% sensitivity for phantom polyps greater than 5mm and 87.5% sensitivity for real polyps greater than 5mm with an average of 4.05 false positives per dataset.

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“…A successful electronic cleansing of the colon lumen is essential for computer aided detection and diagnosis to achieve the goal of massive polyp screening [32,33]. …”

Section: Tagged Materialsmentioning

confidence: 99%

A pilot study on less-stressful bowel preparation for virtual colonoscopy screening with follow-up biopsy by optical colonoscopy

et al. 2005

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Objective:To investigate a less stressful bowel preparation for polyp screening by virtual colonoscopy (VC) with follow-up biopsy on the positive findings by optical colonoscopy (OC). Materials and Methods: Fifty-eight volunteers of age older than 40 --receiving low-residue diet and laxatives of magnesium citrate, bisacodyl tablets and suppository --were divided into three groups. In Group I, 16 volunteers took three 40cc oral doses of MD-Gastroview with the three meals respectively, the day prior to VC procedure. In Group II, 18 volunteers ingested barium sulfate suspension (2% w/v, 250 cc/dose) at bedtime and in the next day morning of VC. In Group III, 24 volunteers received 60 cc of MD-Gastroview at bedtime and in the next day morning of VC. Following colon inflation with CO 2 , computer tomography (CT) abdominal images were acquired by a standard single-slice detector-band VC protocol, i.e., 5 mm collimation, 1 mm reconstruction, 1.5-2.0:1.0 pitch, 120 kVp and 100-150 mA. The CT density of the tagged residual fluid was measured. An image segmentation algorithm was applied to remove electronically the residue fluid. Results: The average fluid density was 97 HU for Group I, 221 HU for Group II2, and 599 HU for Group III. These three groups' density means are significantly different (p < 0.001 one-way ANOVA). After the electronic cleansing, the % of cleansed fluid regions was 5.5%, 16.5% and 93.1% (p<0.0001 Chi square) for these groups respectively. Conclusion: A less-stressful bowel preparation with low residue diet and MD-Gastroview oral contrast is feasible for VC screening with follow-up biopsy on the positive findings by OC.

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Colonic polyp characterization and detection based on both morphological and texture features

Cited by 9 publications

References 14 publications

The use of 3D surface fitting for robust polyp detection and classification in CT colonography

The use of 3D surface fitting for robust polyp detection and classification in CT colonography

A statistical approach for robust polyp detection in CT colonography

A pilot study on less-stressful bowel preparation for virtual colonoscopy screening with follow-up biopsy by optical colonoscopy

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