Improving the Quality of Electronic Cleansing of Colorectal CT Images Using a Hybrid Method

  • Hamid Moghaddasi Associate Professor of Health Information Management & Medical Informatics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences http://orcid.org/0000-0002-5906-0329
  • Hossein Beigi Harchegani Department of Health Information technology, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  • Mahtab Shaebani Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  • Hamid Beigy Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
  • Babak SalavatiPour Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Keywords: Electronic Colon Cleansing, CAD, Polypeptide diagnosis, CT Colonography

Abstract

Introduction: Colorectal cancer, as one of the most important fatal cancers, is caused by the lack of timely diagnosis of colorectal polyps. Presently, because of the advancements in CT imaging of the colorectal device, the CTC-CAD is a promising method for the duly diagnosis of these appendages. In this regard, Electronic Colon Cleansing (ECC) is one of the effective factors that enhance diagnostic accuracy in the methods used in CTC-CAD.

Method: In this study, to implement ECC, the thresholding method, statistical functions, and image processing methods were combined. Then, to evaluate the proposed method, 22 images were randomly selected and ranked by seven radiologists. Regarding the extent of the interpretable, the images taken before and after ECC were collected using MD and LM_ECC methods. The concordance of concordance of all three categories of opinions was calculated based on Kendall’s tau-b correlation coefficient test.

Findings: The value of t-test between the mean score of radiologists' opinions for the main images and the results of the LM_ECC method (p <0.001) is -9.355, while it is -5.414 between the mean score of radiologists for the MD results and the results obtained from the LM_ECC method (p <0.001).

Conclusion: Based on the coefficient of concordance, it is found that there is a high agreement between the ranked opinions of the radiologists, based on which the results of the T-tests show the significant effect of the LM_ECC method on electronic cleansing compared to the main images and the results of MD method.  

References

(1) Moghaddasi et al., “Application of Virtual Reality in Colonoscopy”, Journal of Health Informatics & Management, 2018. 2(1).1000109

(2) Bellini, D., et al., Bowel preparation in CT colonography: Is diet restriction necessary? A randomised trial (DIETSAN). European radiology, 2018. 28(1): p. 382-389.

(3) “Cancer Facts and Figures,” A. C. Society, Ed., ed, 2003.

(4) Virtual Colonoscopy - Technical Aspects: InTech China, 2011.

(5) Y. H. e. al., “Computer-aided Diagnosis Scheme for Detection of Polyps at CT Colonography,” Radiographics, vol. 22, pp. 963- 979, 2002.

(6) L. Li, et al., “An image segmentation approach to extract colon lumen through colonic material tagging and hidden Markov random field model for virtual colonoscopy.”

(7) R. SL, et al., “Patient preferences for CT colonography, conventional colonoscopy and bowel preparation,” Am J Gastroenterol, pp. 578–585, 2003.

(8) L. PA, et al., “ Dietary fecal tagging as a cleansing method before CT colonography 2002;224. ,” Radiology, vol. 224, pp. 393–403, 2002.

(9) D. J. Vining, et al., “Technical feasibility of colon imaging with helical CT and virtual reality,” Ann. Meeting Amer. Roentgen Ray. Soc, p. 104, 1994.

(10) e. a. Cai W., “An Electronic cleansing method for inhomogeneously tagged regions in noncathartic CT colonography.,” vol. 30, pp. 559-574, 2010.

(11) e. a. Wang Z., “An improved electronic colon cleansing method for detection of colonic polyps by virtual colonoscopy,” IEEE Transaction on Biomedical Engineering, vol. 53, pp. 1635-1646, 2006.

(12) S. Lakare, et al., “Electronic colon cleansing using segmentation rays for virtual colonoscopy.”

(13) Y. Zhang, et al., “Segmentation of brain MR images through a

hidden Markov random field model and the expectation-maximization algorithm,” IEEE Trans. Medical Imaging, vol. 20, pp. 45-57, 2001.

(14) D. Chen, et al., “A novel approach to extract colon lumen from CT images for virtual colonoscopy,” IEEE Trans. Medical Imaging, vol. 19, pp. 1220-1226, 2000.

(15) R. Leahy, et al., “Applications of Markov random fields in medical imaging,” Information Processing in Medical Imaging, pp. 1-14, 1991.

(16) K. Held, et al., “ Markov random field segmentation of brain MR images,” IEEE Trans. Medical Imaging, vol. 16, pp. 878-886, 1997.

(17) L. Li, et al., “Segmentation of MR brain images: a self-adaptive online vector quantization approach,” SPIE Medical Imaging, vol. 4322, pp. 1431-1438, 2001.

(18) Z. Liang, et al., “ Parameter estimation and tissue segmentation from multispectral MR images,” IEEE Trans. Medical Imaging, vol. 13, pp. 441-449, 1994.

(19) S. Lakare et al., “3D Digital Cleansing Using Segmentation Rays”, IEEE Visualization, pp. 37–44, 2000.

(20) e. a. Skalski A., “Colon cleansing for virtual colonoscopy using

non-linear transfer function and morphological operations.,” IEEE international workshop on Imaging systems and techniques, pp. 1-5, May

(21) Z. M.E., et al., “Digital subtraction bowel cleansing for CT colonography using morphological and linear filtration methods.,” IEEE Transactions on Medical Imaging, vol. 23, pp. 1335-1343, 2004.

(22) Cowan, G., Statistical data analysis. 1998: Oxford university press.

Published
2019-03-09