Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and Classification Article Swipe
Hui Liu
,
Caiming Zhang
,
Zhiyuan Su
,
Kai Wang
,
Kai Deng
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1155/2015/185726
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1155/2015/185726
The key problem of computer-aided diagnosis (CAD) of lung cancer is to segment pathologically changed tissues fast and accurately. As pulmonary nodules are potential manifestation of lung cancer, we propose a fast and self-adaptive pulmonary nodules segmentation method based on a combination of FCM clustering and classification learning. The enhanced spatial function considers contributions to fuzzy membership from both the grayscale similarity between central pixels and single neighboring pixels and the spatial similarity between central pixels and neighborhood and improves effectively the convergence rate and self-adaptivity of the algorithm. Experimental results show that the proposed method can achieve more accurate segmentation of vascular adhesion, pleural adhesion, and ground glass opacity (GGO) pulmonary nodules than other typical algorithms.
Related Topics
Concepts
Segmentation
Pixel
Artificial intelligence
Pattern recognition (psychology)
Cluster analysis
Computer science
Image segmentation
Grayscale
Computer vision
Similarity (geometry)
Ground-glass opacity
Fuzzy logic
Computer-aided diagnosis
Image (mathematics)
Cancer
Medicine
Internal medicine
Adenocarcinoma
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2015/185726
- https://downloads.hindawi.com/journals/cmmm/2015/185726.pdf
- OA Status
- hybrid
- Cited By
- 14
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2084404353
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2084404353Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1155/2015/185726Digital Object Identifier
- Title
-
Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-01-01Full publication date if available
- Authors
-
Hui Liu, Caiming Zhang, Zhiyuan Su, Kai Wang, Kai DengList of authors in order
- Landing page
-
https://doi.org/10.1155/2015/185726Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/cmmm/2015/185726.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://downloads.hindawi.com/journals/cmmm/2015/185726.pdfDirect OA link when available
- Concepts
-
Segmentation, Pixel, Artificial intelligence, Pattern recognition (psychology), Cluster analysis, Computer science, Image segmentation, Grayscale, Computer vision, Similarity (geometry), Ground-glass opacity, Fuzzy logic, Computer-aided diagnosis, Image (mathematics), Cancer, Medicine, Internal medicine, AdenocarcinomaTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
14Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2022: 2, 2020: 5, 2019: 1, 2018: 2Per-year citation counts (last 5 years)
- References (count)
-
23Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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