Detection of Lesions in Colonoscopic Images: A Review

Colonoscopy is one of the best methods for screening colon cancer and still is the “gold standard” despite advancements in the field of virtual endoscopy based on computer-tomographic imaging. As the automatic detection of polyps in endoscopic images is a challenging task for image processing, a variety of research groups have proposed methods that try to fulfill this task to develop a system which supports the doctors during examination. However, the problem can still not be assumed to be solved. This paper provides a review of the state of the art in detection methods published within the last decade. We found out that the major drawback of many approaches is the lack of representative video data, which hinders comparison and evaluation of the published methods.

[1]  P. Wang,et al.  Classification of endoscopic images based on texture and neural network , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[3]  M. P. Tjoa,et al.  Feature extraction for the analysis of colon status from the endoscopic images , 2003, Biomedical engineering online.

[4]  Shankar M. Krishnan,et al.  Automated diagnosis for segmentation of colonoscopic images using chromatic features , 2002, IEEE CCECE2002. Canadian Conference on Electrical and Computer Engineering. Conference Proceedings (Cat. No.02CH37373).

[5]  Miguel Tavares Coimbra,et al.  MPEG-7 Visual Descriptors—Contributions for Automated Feature Extraction in Capsule Endoscopy , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[6]  R. Doraiswami,et al.  Real-time image processing system for endoscopic applications , 2003, CCECE 2003 - Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No.03CH37436).

[7]  Christoph Palm,et al.  Color Texture Analysis of Moving Vocal Cords Using Approaches from Statistics and Signal Theory , 2000 .

[8]  Dimitris A. Karras,et al.  Detection of lesions in endoscopic video using textural descriptors on wavelet domain supported by artificial neural network architectures , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[9]  S. M. Krishnan,et al.  Quantitative parametrization of colonoscopic images by applying fuzzy technique , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[10]  Dimitris A. Karras,et al.  Computer Methods and Programs in Biomedicine , 2022 .

[11]  Christian Münzenmayer,et al.  Multispectral Texture Analysis Using Interplane Sum- and Difference-Histograms , 2002, DAGM-Symposium.

[12]  Peng Li,et al.  Learning a multi-size patch-based hybrid kernel machine ensemble for abnormal region detection in colonoscopic images , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Jung-Hwan Oh,et al.  Polyp Detection in Colonoscopy Video using Elliptical Shape Feature , 2007, 2007 IEEE International Conference on Image Processing.

[14]  Luís A. Alexandre,et al.  Polyp Detection in Endoscopic Video Using SVMs , 2007, PKDD.

[15]  Shankar M. Krishnan,et al.  Intestinal abnormality detection from endoscopic images , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[16]  George D. Magoulas,et al.  Detecting Abnormalities in Colonoscopic Images by Textural Description and Neural Networks , 1999 .

[18]  Dimitrios K. Iakovidis,et al.  An intelligent system for automatic detection of gastrointestinal adenomas in video endoscopy , 2006, Comput. Biol. Medicine.

[19]  Vassilis P. Plagianakos,et al.  Improved Neural Network-based Interpretation of Colonoscopy Images Through On-line Learning and Evolution , 2001 .

[20]  Dimitris A. Karras,et al.  Computer-aided tumor detection in endoscopic video using color wavelet features , 2003, IEEE Transactions on Information Technology in Biomedicine.

[21]  D. Lieberman.  Quality and colonoscopy: a new imperative. , 2005, Gastrointestinal endoscopy.

[22]  Basanna V. Dhandra,et al.  Analysis of Abnormality in Endoscopic images using Combined HSI Color Space and Watershed Segmentation , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[23]  Christoph Palm,et al.  Color texture classification by integrative Co-occurrence matrices , 2004, Pattern Recognit..

[24]  Marta P. Tjoa,et al.  A fusion-based clinical decision support for disease diagnosis from endoscopic images , 2005, Comput. Biol. Medicine.

[25]  V. P. Plagianakos,et al.  TUMOR DETECTION IN COLONOSCOPY USING THE UNSUPERVISED k-WINDOWS CLUSTERING ALGORITHM AND NEURAL NETWORKS , 2004 .

[26]  Jingsong He,et al.  Colonoscopic miss rates for right-sided colon cancer: a population-based analysis. , 2004, Gastroenterology.

[27]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  George D. Magoulas,et al.  Neural network-based colonoscopic diagnosis using on-line learning and differential evolution , 2004, Appl. Soft Comput..