ArticleJournal of medical signals and sensors
Barrett's Mucosa Segmentation in Endoscopic Images Using a Hybrid Method: Spatial Fuzzy c-mean and Level Set.
Article in Journal of medical signals and sensors. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 6 citations in OpenAlex.
- Tri-Scan: A Three Stage Color Enhancement Tool for Endoscopic Images.Journal of medical systems · 2017Article
- Demarcating Z-line and Gastric Folds Boundary Based on the Segmentation of the Lower Esophageal Sphincter Images.Journal of medical signals and sensorsArticle
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Barrett's mucosa is one of the most important diseases in upper gastrointestinal system that caused by gastro-esophagus reflux. If left untreated, the disease will cause distal esophagus and gastric cardia adenocarcinoma. The malignancy risk is very high in short segment Barrett's mucosa. Therefore, lesion area segmentation can improve specialist decision for treatment. In this paper, we proposed a combined fuzzy method with active models for Barrett's mucosa segmentation. In this study, we applied three methods for special area segmentation and determination. For whole disease area segmentation, we applied the hybrid fuzzy based level set method (LSM). Morphological algorithms were used for gastroesophageal junction determination, and we discriminated Barrett's mucosa from break by applying Chan-Vase method. Fuzzy c-mean and LSMs fail to segment this type of medical image due to weak boundaries. In contrast, the full automatic hybrid method with correlation approach that has used in this paper segmented the metaplasia area in the endoscopy image with desirable accuracy. The presented approach omits the manually desired cluster selection step that needed the operator manipulation. Obtained results convinced us that this approach is suitable for esophagus metaplasia segmentation.
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Identifiers
28028499PMC5156999W2553038918What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.