An Improved Method of Polyp Size Measurement in Computed Tomography Colonography Images

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Size and shape analysis is one of the tasks in automated measurement of colon polyps using Computed Tomography Colonography (CTC) images. The research objective was to measure the smaller polyps using image processing techniques. This paper proposes a knowledge-based approach using a semiautomatic method of colon segmentation and morphological image processing methods. The retrospective study included 35 CTC scans for evaluation. The combined approach of analyzing the polyp from domain perspective and the skeletonization has given good results. It takes ~7 minutes for smaller polyp measurement in a CTC scan of 450-500 images using multithreading on a high-performance computer. The results were statistically tested at α=5% and the measurements were acceptable.

Original languageEnglish
Title of host publicationProceedings of 2017 International Conference on Graphics and Signal Processing, ICGSP 2017
PublisherAssociation for Computing Machinery (ACM)
Pages26-29
Number of pages4
VolumePart F130281
ISBN (Electronic)9781450352390
DOIs
Publication statusPublished - 24-06-2017
Event2017 International Conference on Graphics and Signal Processing, ICGSP 2017 - Singapore, Singapore
Duration: 24-06-201727-06-2017

Conference

Conference2017 International Conference on Graphics and Signal Processing, ICGSP 2017
CountrySingapore
CitySingapore
Period24-06-1727-06-17

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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  • Cite this

    Manjunath, K. N., Siddalingaswamy, P. C., & Prabhu, G. K. (2017). An Improved Method of Polyp Size Measurement in Computed Tomography Colonography Images. In Proceedings of 2017 International Conference on Graphics and Signal Processing, ICGSP 2017 (Vol. Part F130281, pp. 26-29). Association for Computing Machinery (ACM). https://doi.org/10.1145/3121360.3121380