Name
Abstract | ||
|---|---|---|
An automated system for analysis of colonoscopy videos is expected to complement the expertise and the experience of a medical professional in: (a) detecting lesions and (b) assessing the quality of a given procedure. Colonoscopy videos contain a significant number of frames which do not carry any clinical information. The presence of such frames would slow down or cause the failure of the processing steps of such an automated system. Furthermore, many existing metrics to measure the quality of the colonoscopy procedures directly involve the number of such indistinct frames present in the videos. We propose a novel algorithm to detect indistinct frames based on the wavelet analysis. The L2 norm of the detail coefficients of the wavelet decomposition of a colonoscopy image is considered as the feature vector of the proposed classification system. The algorithm was tested on a manually labeled, balanced data set. It achieved an accuracy of 99.59% in a leave-two-out cross validation procedure based on Bayesian classification. Furthermore, when applied to full colonoscopy videos, the presented algorithm detected 26.2% of the frames as indistinct, of which 92.3% were correctly classified. The proposed method outperforms the current best performing algorithm both in terms of accuracy and computation time. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IMVIP.2009.16 | IMVIP |
Keywords | Field | DocType |
novel algorithm,indistinct frame detection,colonoscopy image,automated system,colonoscopy procedure,indistinct frame,proposed classification system,full colonoscopy video,colonoscopy video,leave-two-out cross validation procedure,colonoscopy videos,bayesian classification,cross validation,pixel,wavelet transforms,wavelet analysis,classification system,feature extraction,accuracy,image classification,feature vector,colorectal cancer,wavelet transform,cancer | Computer vision,Feature vector,Pattern recognition,Naive Bayes classifier,Computer science,Feature extraction,Pixel,Artificial intelligence,Contextual image classification,Cross-validation,Wavelet,Wavelet transform | Conference |
Citations | PageRank | References |
9 | 0.69 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mirko Arnold | 1 | 29 | 2.70 |
| Anarta Ghosh | 2 | 156 | 13.81 |
| Gerard Lacey | 3 | 171 | 22.17 |
| Stephen Patchett | 4 | 12 | 1.83 |
| Hugh Mulcahy | 5 | 12 | 1.83 |