Motion artifact detection in colonoscopy images Article Swipe
Rukiye Nur Kaçmaz
,
Bülent Yılmaz
,
Mehmet Sait Dündar
,
Serkan Doğan
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.2478/ebtj-2018-0022
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.2478/ebtj-2018-0022
Computer-aided detection is an integral part of medical image evaluation process because examination of each image takes a long time and generally experts’ do not have enough time for the elimination of images with motion artifact (blurred images). Computer-aided detection is required for both increasing accuracy rate and saving experts’ time. Large intestine does not have straight structure thus camera of the colonoscopy should be moved continuously to examine inside of the large intestine and this movement causes motion artifact on colonoscopy images. In this study, images were selected from open-source colonoscopy videos and obtained at Kayseri Training and Research Hospital. Totally 100 images were analyzed half of which were clear. Firstly, a modified version of histogram equalization was applied in the pre-processing step to all images in our dataset, and then, used Laplacian, wavelet transform (WT), and discrete cosine transform-based (DCT) approaches to extract features for the discrimination of images with no artifact (clear) and images with motion artifact. The Laplacian-based feature extraction method was used for the first time in the literature on colonoscopy images. The comparison between Laplacian-based features and previously used methods such as WT and DCT has been performed. In the classification phase of our study, support vector machines (SVM), linear discriminant analysis (LDA), and k nearest neighbors (k-NN) were used as the classifiers. The results showed that Laplacian-based features were more successful in the detection of images with motion artifact when compared to popular methods used in the literature. As a result, a combination of features extracted using already existing approaches (WT and DCT) and the Laplacian-based methods reached 85% accuracy levels with SVM classification approach
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2478/ebtj-2018-0022
- https://content.sciendo.com/downloadpdf/journals/ebtj/2/3/article-p171.pdf
- OA Status
- diamond
- Cited By
- 1
- References
- 13
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2885946917
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2885946917Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2478/ebtj-2018-0022Digital Object Identifier
- Title
-
Motion artifact detection in colonoscopy imagesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-07-01Full publication date if available
- Authors
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Rukiye Nur Kaçmaz, Bülent Yılmaz, Mehmet Sait Dündar, Serkan DoğanList of authors in order
- Landing page
-
https://doi.org/10.2478/ebtj-2018-0022Publisher landing page
- PDF URL
-
https://content.sciendo.com/downloadpdf/journals/ebtj/2/3/article-p171.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://content.sciendo.com/downloadpdf/journals/ebtj/2/3/article-p171.pdfDirect OA link when available
- Concepts
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Artificial intelligence, Artifact (error), Computer vision, Computer science, Pattern recognition (psychology), Histogram, Support vector machine, Feature extraction, Feature (linguistics), Discrete cosine transform, Linear discriminant analysis, Image (mathematics), Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2021: 1Per-year citation counts (last 5 years)
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13Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.is_published | True |
| primary_location.raw_source_name | The EuroBiotech Journal |
| primary_location.landing_page_url | https://doi.org/10.2478/ebtj-2018-0022 |
| publication_date | 2018-07-01 |
| publication_year | 2018 |
| referenced_works | https://openalex.org/W116236279, https://openalex.org/W4299840051, https://openalex.org/W2082965189, https://openalex.org/W6631253713, https://openalex.org/W2139182968, https://openalex.org/W2006913521, https://openalex.org/W2439058561, https://openalex.org/W1586882688, https://openalex.org/W1965475047, https://openalex.org/W2142634613, https://openalex.org/W2108364340, https://openalex.org/W2149310070, https://openalex.org/W2283997769 |
| referenced_works_count | 13 |
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| abstract_inverted_index.(WT), | 137 |
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| abstract_inverted_index.(SVM), | 205 |
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| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5055416074 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I95293974 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| sustainable_development_goals[1].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[1].score | 0.44999998807907104 |
| sustainable_development_goals[1].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.57258988 |
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| citation_normalized_percentile.is_in_top_10_percent | False |