Domain-Based Analysis of Colon Polyp in CT Colonography Using Image-Processing Techniques

Document Type : Research Articles

Authors

1 Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.

2 Department of Electronics and Communication, Manipal University, Jaipur, India.

Abstract

Background: The purpose of the research was to improve the polyp detection accuracy in CT Colonography (CTC)
through effective colon segmentation, removal of tagged fecal matter through Electronic Cleansing (EC), and measuring
the smaller polyps. Methods: An improved method of boundary-based semi-automatic colon segmentation with the
knowledge of colon distension, an adaptive multistep method for the virtual cleansing of segmented colon based on
the knowledge of Hounsfield Units, and an automated method of smaller polyp measurement using skeletonization
technique have been implemented. Results: The techniques were evaluated on 40 CTC dataset. The segmentation
method was able to delineate the colon wall accurately. The submerged colonic structures were preserved without
soft tissue erosion, pseudo enhanced voxels were corrected, and the air-contrast layer was removed without losing
the adjacent tissues. The smaller polyp of size less than validated qualitatively and quantitatively. Segmented colons were validated through volumetric overlap computation,
and accuracy of 95.826±0.6854% was achieved. In polyp measurement, the paired t-test method was applied to compare
the difference with ground truth and at α=5%, t=0.9937 and p=0.098 was achieved. The statistical values of TPR=90%,
TNR=82.3% and accuracy=88.31% were achieved. Conclusion: An automated system of polyp measurement has been
developed starting from colon segmentation to improve the existing CTC solutions. The analysis of domain-based
approach of polyp has given good results. A prototype software, which can be used as a low-cost polyp diagnosis tool,
has been developed.

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Asian Pacific Journal of Cancer Prevention
Volume 20, Issue 2
February 2019
Pages 629-637

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History

  • Receive Date: 05 October 2018
  • Revise Date: 27 November 2018
  • Accept Date: 21 January 2019

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