DOI QR코드

DOI QR Code

Automatic Colorectal Polyp Detection in Colonoscopy Video Frames

  • Geetha, K (Department of Information Technology, Excel Engineering College) ;
  • Rajan, C (Department of Information Technology, K S Rangasamy College of Technology)
  • Published : 2016.11.01

Abstract

Colonoscopy is currently the best technique available for the detection of colon cancer or colorectal polyps or other precursor lesions. Computer aided detection (CAD) is based on very complex pattern recognition. Local binary patterns (LBPs) are strong illumination invariant texture primitives. Histograms of binary patterns computed across regions are used to describe textures. Every pixel is contrasted relative to gray levels of neighbourhood pixels. In this study, colorectal polyp detection was performed with colonoscopy video frames, with classification via J48 and Fuzzy. Features such as color, discrete cosine transform (DCT) and LBP were used in confirming the superiority of the proposed method in colorectal polyp detection. The performance was better than with other current methods.

Keywords

Colon cancer;colonoscopy;Local Binary Pattern (LBP);J48;Fuzzy;Discrete Cosine Transform (DCT)

References

  1. Bhuvana S, Bhuvaneswari AJ (2015). Computer aided automatic detection of polyp for colon tumor. Comput Med Imaging Graph, 4, 431-38.
  2. Filipe JC, Condessa AND. (2011). Detection and Classification of Human Colorectal Polyps. Comput Med Imaging Graph, 10, 1-10.
  3. Fu JJ, Yu YW, Lin HM, Chai JW, Chen CCC (2014). Feature extraction and pattern classification of colorectal polyps in colonoscopic imaging. Comput Med Imaging Graph, 38, 267-75. https://doi.org/10.1016/j.compmedimag.2013.12.009
  4. Gasparovica M, Aleksejeva L (2011). Using fuzzy unordered rule induction algorithm for cancer data classification. Breast Cancer, 13, 1-7.
  5. Huhn J, Hullermeier E (2009). FURIA: An algorithm for unordered fuzzy rule induction. Data Min Knowl Discov, 19, 293-319. https://doi.org/10.1007/s10618-009-0131-8
  6. Jabid T, Kabir MH, Chae O (2010). Robust facial expression recognition based on local directional pattern. ETRI J Journal, 32, 784-.94 https://doi.org/10.4218/etrij.10.1510.0132
  7. Kaur G, Chhabra A (2014). Improved J48 classification algorithm for the prediction of diabetes. Int j comput appl technol, 98, 7-.13.
  8. Li J, Tan J, Martz FA, Heymann H (1999). Image texture features as indicators of beef tenderness. Meat Sci, 53, 17-22. https://doi.org/10.1016/S0309-1740(99)00031-5
  9. Liu J, McCloskey S, LiuY (2012). Local expert forest of score fusion for video event classification. In computer vision-ECCV 2012. Springer Berlin Heidelberg, pp. 397-410.
  10. Mamonov AV, Figueiredo IN, Figueiredo PN, Tsai YHR (2014). Automated polyp detection in colon capsule endoscopy. IEEE Trans Med Imaging, 33, 1488-1502. https://doi.org/10.1109/TMI.2014.2314959
  11. Manivannan S (2015). Visual feature learning with application to medical image classification (Doctoral dissertation, University of Dundee). pp 1-154
  12. Manivannan S, Trucco E (2015). Learning discriminative local features from image-level labelled data for colonoscopy image classification. In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) pp 420-23.
  13. Muthukudage J, Oh J, Tavanapong W, Wong J, De Groen PC (2011). Color based stool region detection in colonoscopy videos for quality measurements. In Pacific-Rim Symposium on Image and Video Technology. Springer Berlin Heidelberg pp 61-72.
  14. Riley SA (2008). Colonoscopic polypectomy and endoscopic mucosal resection: a practical guide. Br Soc Gastroenterol, 8, 1-22.
  15. Tajbakhsh N, Gurudu SR, Liang J (2016). Automated polyp detection in colonoscopy videos using shape and context information. IEEE Trans Med Imaging, 35, 630-44. https://doi.org/10.1109/TMI.2015.2487997
  16. Zhang Y, Hao P, Wang M, Guo C (2011). Research on classification and detection of colon cancer's gene expression profiles. Biomed Res Int, 6, 2792-800.