Barrett's Mucosa Segmentation in Endoscopic Images Using a Hybrid Method: Spatial Fuzzy c-mean and Level Set

Abstract

Barrett's mucosa is one of the most important diseases in upper gastrointestinal system that caused by gastroesophagus
reflux. If left untreated, the disease will cause distal esophagus and gastric cardiaadenocarcinoma. 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 proposeda 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 areasegmentation, we applied the hybrid fuzzy based level set method (LSM). Morphological algorithmswere used for gastroesophageal junction determination, and we discriminated Barrett’s mucosa from breakby applying Chan-Vase method. Fuzzy c-mean and LSMs fail to segment this type of medical imagedue to weak boundaries. In contrast, the full automatic hybrid method with correlation approachthat has used in this paper segmented the metaplasia area in the endoscopy image with desirableaccuracy. The presented approach omits the manually desired cluster selection step that needed theoperator manipulation. Obtained results convinced us that this approach is suitable for esophagusmetaplasia segmentation.