Low grade glioma segmentation using an automatic computational technique in magnetic resonance imaging Article Swipe
Miguel Vera
,
Yoleidy Huérfano
,
Oscar Valbuena
,
Yudith Contreras
,
María Juliana Santaella Cuberos
,
Marisela Vivas
,
Williams Salazar
,
María Isabel Vera
,
Maryury Borrero
,
Carlos Hernández
,
Doris Barrera
,
Ángel Valentín Molina
,
Luis Javier Martínez
,
Juan Salazar
,
E Gelvez-Almeida
,
Frank Sáenz
·
YOU?
·
· 2018
· Open Access
·
YOU?
·
· 2018
· Open Access
·
"Through this work we propose a computational technique for the segmentation of a brain tumor, identified as low grade glioma (LGG), specifically grade II astrocytoma, which is present in magnetic resonance images (MRI). This technique consists of 3 stages developed in the three-dimensional domain. They are: pre-processing, segmentation and post-processing. The percent relative error (PrE) is considered to compare the segmentations of the LGG, generated by a neuro-oncologist manually, with the dilated segmentations of the LGG, obtained automatically. The combination of parameters linked to the lowest PrE, allow establishing the optimal parameters of each computational algorithm that makes up the proposed computational technique. The results allow reporting a PrE of 1.43%, which indicates an excellent correlation between the manual segmentations and those produced by the computational technique developed."
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Metadata
- Type
- article
- Language
- en
- Landing Page
- http://hdl.handle.net/20.500.12442/2525
- http://hdl.handle.net/20.500.12442/2525
- OA Status
- gold
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2919661850
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2919661850Canonical identifier for this work in OpenAlex
- Title
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Low grade glioma segmentation using an automatic computational technique in magnetic resonance imagingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-01-01Full publication date if available
- Authors
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Miguel Vera, Yoleidy Huérfano, Oscar Valbuena, Yudith Contreras, María Juliana Santaella Cuberos, Marisela Vivas, Williams Salazar, María Isabel Vera, Maryury Borrero, Carlos Hernández, Doris Barrera, Ángel Valentín Molina, Luis Javier Martínez, Juan Salazar, E Gelvez-Almeida, Frank SáenzList of authors in order
- Landing page
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https://hdl.handle.net/20.500.12442/2525Publisher landing page
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https://hdl.handle.net/20.500.12442/2525Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://hdl.handle.net/20.500.12442/2525Direct OA link when available
- Concepts
-
Magnetic resonance imaging, Segmentation, Artificial intelligence, Computer science, Pattern recognition (psychology), Nuclear magnetic resonance, Computer vision, Physics, Medicine, RadiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.parameters | 81, 91 |
| abstract_inverted_index.technique. | 102 |
| abstract_inverted_index.combination | 79 |
| abstract_inverted_index.correlation | 115 |
| abstract_inverted_index.developed." | 127 |
| abstract_inverted_index.astrocytoma, | 24 |
| abstract_inverted_index.establishing | 88 |
| abstract_inverted_index.segmentation | 10, 47 |
| abstract_inverted_index.specifically | 21 |
| abstract_inverted_index.computational | 6, 94, 101, 125 |
| abstract_inverted_index.segmentations | 60, 72, 119 |
| abstract_inverted_index.automatically. | 77 |
| abstract_inverted_index.pre-processing, | 46 |
| abstract_inverted_index.neuro-oncologist | 67 |
| abstract_inverted_index.post-processing. | 49 |
| abstract_inverted_index.three-dimensional | 42 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 16 |
| citation_normalized_percentile.value | 0.18480202 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |