Motion artifact detection in colonoscopy images Cover

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Motion artifact detection in colonoscopy images

The EuroBiotech Journal

Volume 2 (2018): Issue 3 (July 2018)

By: Rukiye Nur Kaçmaz, Bülent Yılmaz, Mehmet Sait Dündar and Serkan Doğan

Open Access

|Jul 2018

Figures & Tables

Sample colonoscopy images from our database are shown as clear (a, b, c) and with motion artifact (d, e, f).

Original colonoscopy image (a), gray-scale representation of the original image (b), and the resultant image of adaptive histogram equalization applied on gray-scale image (c).

Automatic clear vs_ blurred image discrimination accuracies, specificities, f-measure scales, sensitivities and AUC for different feature extraction methods and classification approaches_

Measurement Performance
	Classification Methods
	SVM				LDA				k-NN
	LAP*	WT	DCT	All	LAP*	WAV	DCT	All	LAP*	WAV	DCT	All
Accuracy	%76	%71	%66	%85	%72	%67	%69	%85	%72	%64	%63	%70
Specificity	%72	%86	%62	%84	%76	%72	%62	%82	%82	%78	%70	%78
f-measure	0.76	0.61	0.65	0.85	0.72	0.66	0.68	0.85	0.70	0.61	0.52	0.69
Sensitivity	%80	%56	%70	%86	%68	%62	%76	%88	%62	%50	%56	%62
AUC	0.76	0.76	0.72	0.88	0.72	0.71	0.72	0.88	0.71	0.62	0.66	0.71

DOI: https://doi.org/10.2478/ebtj-2018-0022 | Journal eISSN: 2564-615X

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Language: English

Page range: 171 - 175

Published on: Jul 31, 2018

Published by: European Biotechnology Thematic Network Association

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Image processing,

motion-artifact,

Related subjects:

Bioinformatics,

Life sciences, other

© 2018 Rukiye Nur Kaçmaz, Bülent Yılmaz, Mehmet Sait Dündar, Serkan Doğan, published by European Biotechnology Thematic Network Association
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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