Skip to main content

Advertisement

SpringerLink
Log in

A CAD of fully automated colonic polyp detection for contrasted and non-contrasted CT scans

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Computer-aided detection (CAD) systems are developed to help radiologists detect colonic polyps over CT scans. It is possible to reduce the detection time and increase the detection accuracy rates by using CAD systems. In this paper, we aimed to develop a fully integrated CAD system for automated detection of polyps that yields a high polyp detection rate with a reasonable number of false positives.

Methods

The proposed CAD system is a multistage implementation whose main components are: automatic colon segmentation, candidate detection, feature extraction and classification. The first element of the algorithm includes a discrete segmentation for both air and fluid regions. Colon-air regions were determined based on adaptive thresholding, and the volume/length measure was used to detect air regions. To extract the colon-fluid regions, a rule-based connectivity test was used to detect the regions belong to the colon. Potential polyp candidates were detected based on the 3D Laplacian of Gaussian filter. The geometrical features were used to reduce false-positive detections. A 2D projection image was generated to extract discriminative features as the inputs of an artificial neural network classifier.

Results

Our CAD system performs at 100% sensitivity for polyps larger than 9 mm, 95.83% sensitivity for polyps 6–10 mm and 85.71% sensitivity for polyps smaller than 6 mm with 5.3 false positives per dataset. Also, clinically relevant polyps (\(\ge \)6 mm) were identified with 96.67% sensitivity at 1.12 FP/dataset.

Conclusions

To the best of our knowledge, the novel polyp candidate detection system which determines polyp candidates with LoG filters is one of the main contributions. We also propose a new 2D projection image calculation scheme to determine the distinctive features. We believe that our CAD system is highly effective for assisting radiologist interpreting CT.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Siegel R, Ma J, Zou Z, Jemal A (2014) Cancer statistics. CA Cancer J Clin 64(1):9–29

    Article  PubMed  Google Scholar 

  2. Bond JH (2000) Clinical evidence for the adenoma-carcinoma sequence and the management of patients with colorectal adenomas. Semin Gastrointest Dis 11(4):176–184

    CAS  PubMed  Google Scholar 

  3. Ransohoff DF (2009) How much does colonoscopy reduce colon cancer mortality. Ann Intern Med 150(1):50–52

    Article  PubMed  Google Scholar 

  4. Chen D, Fahmi R, Farag AA, Falk RL, Dryden GW (2009) Accurate and fast 3D colon segmentation in CT colonography. In: Proceedings of the IEEE international symposium on biomedical imagining: from nano to macro (ISBI 2009), pp 490–493

  5. Van Uitert RL, Summers RM (2007) Automatic correction of level set based subvoxel precise centerlines for virtual colonoscopy using the colon outer wall. IEEE Trans Med Imaging 26(8):1069–1078. doi:10.1109/TMI.2007.896927

    Article  PubMed  Google Scholar 

  6. Jiang Y, Meng J, Jaffer N (2007) A novel segmentation and navigation method for polyps detection using mathematical morphology and active contour models. In: 6th IEEE international conference on cognitive informatics (ICCI), pp 357–363

  7. Sato M, Lakare S, Wan M, Kaufman A, Liang Z, Wax M (2000) An automatic colon segmentation for 3D virtual colonoscopy. IEICE Trans Inf Syst E84–D(1):201–208

    Google Scholar 

  8. Chowdhury TA, Whelan PF, Ghita O (2008) A fully automatic CAD-CTC system based on curvature analysis for standard and low-dose CT data. IEEE Trans Biomed Eng 55(3):888–901

    Article  PubMed  Google Scholar 

  9. Liu J, Wang S, Kabadi S, Summers RM (2009) High performance computer aided detection system for polyp detection in CT colonography with fluid and fecal tagging. In: Proceedings of SPIE 7260, pp 72601B1–72601B7. doi:10.1117/12.811654

  10. Acar B, Beaulieu CF, Göktürk SB, Tomasi C, Paik DS, Jeffrey RB Jr, Yee J, Napel S (2002) Edge displacement field-based classification for improved detection of polyps in CT colonography. IEEE Trans Med Imaging 21(12):1461–1467

    Article  PubMed  Google Scholar 

  11. Ong JL, Seghouane AK, Osborn K (2008) Mean shape models for polyp detection in CT colonography. In: Digital image computing: techniques and applications (DICTA), pp 287–293. doi: 10.1109/DICTA.2008.9

  12. Paik DS, Beaulieu CF, Rubin GD, Acar B, Jeffrey RB, Yee J, Dey J, Napel S (2004) Surface normal overlap: a computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT. IEEE Trans Med Imaging 23(6):661–675

    Article  PubMed  Google Scholar 

  13. Suzuki K, Zhang J, Xu J (2010) Massive-training artificial neural network coupled with Laplacian-eigenfunction-based dimensionality reduction for computer-aided detection of polyps in CT colonography. IEEE Trans Med Imaging 29(11):1907–1917

    Article  PubMed  PubMed Central  Google Scholar 

  14. Xu JW, Suzuki K (2014) Max-AUC feature selection in computer-aided detection of polyps in CT colonography. IEEE J Biomed Health Inform 18(2):585–593

    Article  PubMed  PubMed Central  Google Scholar 

  15. Yao J, Miller M, Franaszek M, Summers RM (2004) Colonic polyp segmentation in CT colonography-based on fuzzy clustering and deformable models. IEEE Trans Med Imaging 23(11):1344–1352

    Article  PubMed  Google Scholar 

  16. Vos FM, Serlie IWO, Van Gelder RE, Post FH, Truyen R, Gerritsen FA, Stoker J, Vossepoel AM (2001) A new visualization method for virtual colonoscopy. In: Proceedings of the 4th international conference on medical image computing and computer-assisted intervention (MICCAI 2001), vol 2208, pp 645–654

  17. Williams D, Codreanu V, Roerdink Jos BTM, Yang P, Liu B, Dong F, Chiarini A (2013) Accelerating colonic polyp detection using commodity graphics hardware. In: Proceedings of the international conference on computer medical applications (ICCMA), pp 1–6. doi:10.1109/ICCMA.2013.6506147

  18. Yoshida H, Masutani Y, MacEneaney P, Rubin DT, Dachman AH (2002) Computerized detection of colonic polyps at CT colonography on the basis of volumetric features: pilot study. Radiology 222(2):327–336. doi:10.1148/radiol.2222010506

    Article  PubMed  Google Scholar 

  19. Vining DJ, Ge Y, Ahn DK, Stelts DR (1999) Virtual colonoscopy with computer assisted polyp detection. In: Hoffmann KR (ed) Computer-aided diagnosis in medical imaging: in proceedings of the first international workshop on computer-aided diagnosis. Elsevier, Amsterdam, pp 445–452

    Google Scholar 

  20. Nappi J, Frimmel H, Dachman A, Yoshida H (2004) Computerized detection of colorectal masses in CT colonography based on fuzzy merging and wall-thickening analysis. Med Phys 31:860–872. doi:10.1118/1.1668591

    Article  PubMed  Google Scholar 

  21. Summers RM, Beaulieu CF, Pusanik LM, Malley JD, Jeffrey RB Jr, Glazer DI, Napel S (2000) Automated polyp detector for CT colonography: feasibility study. Radiology 216(1):284–290

    Article  CAS  PubMed  Google Scholar 

  22. Summers RM, Johnson CD, Pusanik LM, Malley JD, Youssef AM, Reed JE (2001) Automated polyp detection at CT colonography: feasibility assessment in a human population. Radiology 219:51–59. doi:10.1148/radiology.219.1.r01ap0751

    Article  CAS  PubMed  Google Scholar 

  23. Kiss G, Van Cleynenbreugel J, Thomeer M, Suetens P, Marchal G (2002) Computer-aided diagnosis in virtual colonography via combination of surface normal and sphere fitting methods. Eur Radiol 12(1):77–81. doi:10.1007/s003300101040

    Article  PubMed  Google Scholar 

  24. Tomasi C, Göktürk SB (2000) A graph method for the conservative detection of polyps in the colon. In: 2nd international symposium on virtual colonoscopy, Boston

  25. Ong JL, Seghouane AK (2011) From point to local neighborhood: polyp detection in CT colonography using geodesic ring neighborhoods. IEEE Trans Image Process 20(4):1000–1010

    Article  PubMed  Google Scholar 

  26. Göktürk SB, Tomasi C, Acar B, Beaulieu CF, Paik DS, Jeffrey RB Jr, Yee J, Napel S (2001) A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonography. IEEE Trans Med Imaging 20(12):1251–1260. doi:10.1109/42.974920

    Article  PubMed  Google Scholar 

  27. Ong JL, Seghouane AK (2011) Feature selection using mutual information in CT colonography. Pattern Recognit Lett 32(2):337–341

    Article  Google Scholar 

  28. Zheng Y, Yang X, Beddoe G (2007) Reduction of false positives in polyp detection using weighted support vector machines. In: Proceedings of the 29th annual international conference of the IEEE Eng Med Biol Soc (EMBS), pp 4433–4436. doi:10.1109/IEMBS.2007.4353322

  29. Näppi J, Yoshida H (2007) Fully automated three-dimensional detection of polyps in fecal-tagging CT colonography. Acad Radiol 14(3):287–300. doi:10.1016/j.acra.2006.11.007

    Article  PubMed  PubMed Central  Google Scholar 

  30. Jerebko AK, Malley JD, Franaszek M, Summers RM (2003) Multiple neural network classification scheme for detection of colonic polyps in CT colonography datasets. Acad Radiol 10(2):154–160

    Article  PubMed  Google Scholar 

  31. Yoshida H, Näppi J, MacEneaney P, Rubin DT, Dachman AH (2002) Computer-aided diagnosis scheme for detection of polyps at CT colonography. Radiographics 22(4):963–979. doi:10.1148/radiographics.22.4.g02jl16963

    Article  PubMed  Google Scholar 

  32. Näppi J, Yoshida H (2003) Feature-guided analysis for reduction of false positives in CAD of polyps for computed tomographic colonography. Med Phys 30(7):1592–1601. doi:10.1118/1.1576393

    Article  PubMed  Google Scholar 

  33. Miller MT, Pickhardt PJ, Franaszek M, Choi JR, Schindler WR, Summers RM (2004) Assessment of bowel opacification on oral contrast-enhanced CT colonography: multi-institutional trial. Abdominal radiology course syllabus, Houston. Society of Gastrointestinal Radiologists and Society of Uroradiology, TX, pp 34–35

  34. Li J, Franaszek M, Petrick N, Yao J, Huang A, Summers RM (2006) Wavelet method for CT colonography computer-aided polyp detection. In: Proceedings of the IEEE international symposium on biomedical imagining: from nano to macro (ISBI 2006), pp 1316–1319. doi:10.1109/ISBI.2006.1625168

  35. Li J, Van Uitert R, Yao J, Petrick N, Franaszek M, Huang A, Summers RM (2008) Wavelet method for CT colonography computer-aided polyp detection. Med Phys 35(8):3527–3538. doi:10.1118/1.2938517

    Article  PubMed  PubMed Central  Google Scholar 

  36. Yao J, Li J, Summers RM (2007) CT colonography computer-aided polyp detection using topographical height map. In: Proceedings of the IEEE international conference on image processing (ICIP 2007), pp 21–24. doi:10.1109/ICIP.2007.4379754

  37. Yao J, Frentz S, Li J, Summers RM (2008) Polyp height and width measurement using topographic height map. In: Proceedings of SPIE 6916, pp 69160B1–69160B10. doi: 10.1117/12.769463

  38. Yao J, Li J, Summers RM (2009) Employing topographical height map in colonic polyp measurement and false positive reduction. Pattern Recognit 42(6):1029–1040. doi:10.1016/j.patcog.2008.09.034

    Article  PubMed  PubMed Central  Google Scholar 

  39. Miller VC (1953) A quantitative geomorphic study of drainage basin characteristics in the clinch mountain area, Virginia and Tennessee. Technical Report 3, Department of Geology, Columbia University, New York

  40. Yoshida H, Nappi J (2002) Automated detection of polyps with CT colonography: evaluation of volumetric features for reduction of false-positive findings. Acad Radiol 9(4):386–397. doi:10.1016/S1076-6332(03)80184-8

    Article  PubMed  Google Scholar 

  41. Hagan MT, Menhaj MB (1994) Training feed forward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):989–993. doi:10.1109/72.329697

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electrical and Electronics Engineering, Istanbul Yeni Yuzyil University Istanbul, Istanbul, Turkey

    Gökalp Tulum

  2. Electronics and Communication Engineering, Yildiz Technical University Istanbul, Istanbul, Turkey

    Bülent Bolat

  3. Electrical and Electronics Engineering, Istanbul Arel University, Istanbul, Turkey

    Onur Osman

Authors
  1. Gökalp Tulum
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bülent Bolat
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Onur Osman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gökalp Tulum.

Ethics declarations

Funding

The study was not funded by any company.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tulum, G., Bolat, B. & Osman, O. A CAD of fully automated colonic polyp detection for contrasted and non-contrasted CT scans. Int J CARS 12, 627–644 (2017). https://doi.org/10.1007/s11548-017-1521-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-017-1521-9

Keywords

Search

Navigation