Predicting the histology of colorectal lesions in a probabilistic framework

@article{Kwitt2010PredictingTH,
  title={Predicting the histology of colorectal lesions in a probabilistic framework},
  author={Roland Kwitt and Andreas Uhl and Michael H{\"a}fner and Alfred Gangl and Friedrich Wrba and Andreas V{\'e}csei},
  journal={2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops},
  year={2010},
  pages={103-110}
}
  • R. Kwitt, A. Uhl, A. Vécsei
  • Published 13 June 2010
  • Computer Science
  • 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops
In this paper, we present a novel approach to predict the histological diagnosis of colorectal lesions from high-magnification colonoscopy images by means of Pit Pattern analysis. Motivated by the shortcomings of discriminant classifier approaches, we present a generative model based strategy which is closely related to content-based image retrieval (CBIR) systems. The ingredients of the approach are the Dual-Tree Complex Wavelet Transform (DTCWT) and the mathematical construct of copulas. Our… 

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References

SHOWING 1-10 OF 41 REFERENCES
Improving Pit-Pattern Classification of Endoscopy Images by a Combination of Experts
TLDR
This work discusses computer-aided pit-pattern classification of surface structures observed during high-magnification colonoscopy in order to support dignity assessment of colonic polyps and proposes a novel classifier combination approach which is similar to a combination of experts.
A comparative study of texture features for the discrimination of gastric polyps in endoscopic video
TLDR
The results advocate the feasibility of a computer-based system for polyp detection in video gastroscopy that exploits the textural characteristics of the gastric mucosa in conjunction with its color appearance.
Computer-aided tumor detection in endoscopic video using color wavelet features
TLDR
An approach to the detection of tumors in colonoscopic video based on a new color feature extraction scheme to represent the different regions in the frame sequence based on the wavelet decomposition, reaching 97% specificity and 90% sensitivity.
CoLD: a versatile detection system for colorectal lesions in endoscopy video-frames
An intelligent system for automatic detection of gastrointestinal adenomas in video endoscopy
Detection of lesions in endoscopic video using textural descriptors on wavelet domain supported by artificial neural network architectures
TLDR
A framework for classification of suspicious lesions using the video produced during an endoscopic session is presented, based on a feature extraction scheme that uses second order statistical information of the wavelet transformation.
Magnifying colonoscopy in differentiating neoplastic from nonneoplastic colorectal lesions
TLDR
The pit pattern analysis of colorectal lesions by magnifying colonoscope combined with indigocarmine dye is a useful and objective tool for differentiating neoplastic from nonneoplastic lesions of the large bowel.
Pit Pattern Classification of Zoom-Endoscopic Colon Images using Histogram Techniques
Histogram-based techniques for an automated classification of magnifying endoscope images with respect to pit patterns of colon lesions are discussed and compared. Currently, the results only allow a
Colorectal tumours and pit pattern.
TLDR
There were associations between individual pits and crypts in colorectal tumours and there was a correlation between pit pattern and the structure of the underlying crypt or gland.
...
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4
5
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