Automatic Electronic Cleansing in Computed Tomography Colonography Images using Domain Knowledge

  • Manjunath, KN (Biomedical Engineering, Manipal Institute of Technology, Manipal University) ;
  • Siddalingaswamy, PC (Computer Science & Engineering, Manipal Institute of Technology, Manipal University) ;
  • Prabhu, GK (Biomedical Engineering, Manipal Institute of Technology, Manipal University)
  • Published : 2016.01.11


Electronic cleansing is an image post processing technique in which the tagged colonic content is subtracted from colon using CTC images. There are post processing artefacts, like: 1) soft tissue degradation; 2) incomplete cleansing; 3) misclassification of polyp due to pseudo enhanced voxels; and 4) pseudo soft tissue structures. The objective of the study was to subtract the tagged colonic content without losing the soft tissue structures. This paper proposes a novel adaptive method to solve the first three problems using a multi-step algorithm. It uses a new edge model-based method which involves colon segmentation, priori information of Hounsfield units (HU) of different colonic contents at specific tube voltages, subtracting the tagging materials, restoring the soft tissue structures based on selective HU, removing boundary between air-contrast, and applying a filter to clean minute particles due to improperly tagged endoluminal fluids which appear as noise. The main finding of the study was submerged soft tissue structures were absolutely preserved and the pseudo enhanced intensities were corrected without any artifact. The method was implemented with multithreading for parallel processing in a high performance computer. The technique was applied on a fecal tagged dataset (30 patients) where the tagging agent was not completely removed from colon. The results were then qualitatively validated by radiologists for any image processing artifacts.


Oral contrast;Colon segmentation;Hounsfield unit;Domain knowledge;Electronic cleansing


  1. html. Pickhardt PJ, Bakke J, Kuo J, et al (2014). Volumetric analysis of colonic distention according to patient position at CT colonography: diagnostic value of the right lateral decubitus series. AJR Am J Roentgenol, 203, 623-8.
  2. Poullos PD, Beaulieu CF (2010). Current techniques in the performance, interpretation, and reporting of CT colonography. Gastrointest Endosc Clin N Am, 20, 169-92.
  3. Rakesh S (2011). Recent advances in intestinal imaging. Indian J Radiol Imaging, 21, 170-5.
  4. Report 44 (2015). Tissue substitutes in radiation dosimetry and measurement. international commission of radiation units and measurement.
  5. Serli IW, Vos FM, Truyen R, et al (2010). Electronic cleansing for computed tomography (CT) colonography using a scale-invariant three-material model. IEEE Trans Biomed Eng, 57, 1306-17.
  6. Simone P, Faccioli N, Zaccarella A et al (2010). The diagnostic contribution of CT volumetric rendering techniques in routine practice. Indian J Radiol Imaging, 20, 92-7.
  7. Slater A, Taylor SA, Burling D, et al (2006). Colonic polyps: effect of attenuation of tagged fluid and viewing window on conspicuity and measurement-in vitro experiment with porcine colonic specimen. Radiology. 240, 101-9.
  8. The DICOM chapter 3 (2012). PS 3.3. NEMA, USA, 390-1174.
  9. Tian SF, Liu AL, Wang HQ et al (2015). Virtual non-contrast computer tomography (CT) with spectral CT as an alternative to conventional unenhanced ct in the assessment of gastric cancer. Asian Pac J Cancer Prev, 16, 2521-6.
  10. Wang Z, Li X, Li L, et al (2006). An improved electronic colon cleansing method for detection of polyps by virtual colonoscopy. In Conf Proc IEEE Eng Med Biol Soc, 6, 6512-5.
  11. Yamamoto S, Iinuma G, Suzuki M, et al (2009). A simple image processing approach for electronic cleansing in computed tomographic colonography. Biomed Imaging Interv J, 5, 1-5.
  12. Zhang H, Li L, Zhu H, et al. (2014). Integration of 3D scale-based pseudo-enhancement correction and partial volume image segmentation for improving electronic colon cleansing in CT colonography. J Xray Sci Technol, 22, 271-83.
  13. Zalis ME, Perumpillichira J, Hahn PF (2004). Digital subtraction bowel cleansing for CT colonography using morphological and linear filtration methods. IEEE Trans Med Imaging. 23, 1335-43.
  14. Cai W, Yoshida H, Zalis ME et al. (2010). Electronic cleansing for noncathartic CT colonography: a structure-analysis scheme. Informatics in radiology, Radiographics, 30, 585-602.
  15. Chu LL, Weinstein S, Yee J (2011). Colorectal Cancer Screening in Women: An Underutilized Lifesaver. AJR Am J Roentgenol, 196, 303-10.
  16. Clark K, Vendt B, Smith K, et al (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. J Digital Imaging, 26, 1045-1057.
  17. Ca W, Lee JG, Zalis ME, Yoshida M (2011). Mosaic decomposition: an electronic cleansing method for in homogeneously tagged regions in noncathartic CT colonography. IEEE Trans Med Imaging, 30, 559-74.
  18. Ca W, Ki SH, Lee JG, Yoshida H (2013). Dual-Energy Electronic Cleansing for Fecal-Tagging CT Colonography. Radiographics, 33, 891-2.
  19. Group Investigators University College London (2007). Effect of directed training on reader performance for CT colonography: multicenter study. Radiol, 242, 152-61.
  20. Huda W (2015). CT Radiation Exposure: An Overview. Current Radiology Reports. 1-16.
  21. Iinuma G, Miyake M, Arai Y, et al (2008). CT colonographytowards applications for colorectal cancer screening. Asian Pac J Cancer Prev, 9, 833-40.
  22. Johnson D (2010). ACRIN 6664 National CT colonography Trial", american college of radiology imaging network. 8-12.
  23. Johnson CD, MMM, Chen MH et al (2008). Accuracy of CT colonography for detection of large adenomas and cancers. N Engl J Med, 359, 1207-17.
  24. Kalender W (2006). X-ray computed tomography. Phys Med Biol, 51, 29-43.
  25. Kim DH, Pickhardt PJ (2010). Radiologists should read CT colonography. Gastrointest Endosc Clin N Am, 20, 259-69.
  26. Lee H, Lee J, Kim B, Kim SH, Shin YG (2014). Fast threematerial modeling with triple arch projection for electronic cleansing in CTC. IEEE Trans Biomed Eng, 61, 2102-11.
  27. Li XB, Ye ZX (2015). Primary thyroid lymphoma: multi-slice computed tomography findings. Asian Pac J Cancer Prev. 16, 1135-8.
  28. Mah P, Reeves TE, McDavid WD (2010). Deriving Hounsfield units using grey levels in cone beam computed tomography. Dentomaxillofac Radiol, 39, 323-35.
  29. Manjunath KN, Swamy PC, Prabhu GK (2014). Feasibility of computed tomography colonography as a diagnostic procedure in colon cancer screening in India. Asian Pac J Cancer Prev, 15, 5111-6.
  30. Melancon G, Munzer T, Weikopf D (2010). Volume rendering on server GPUs for enterprise scale medical applications. In Eurographics/IEEE-VGTC symposium on Visualization. 1-10.
  31. National Cancer Institute (2015). [Online]:
  32. National Institute of Standards and Technology (2015, ) [Online]: