Yousefi-Banaem Hossein, Rabbani Hossein, Adibi Peyman
Department of Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Science, Isfahan, Iran.
Department of Biomedical Engineering, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
J Med Signals Sens. 2016 Oct-Dec;6(4):231-236.
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.
巴雷特黏膜是上消化道系统中由胃食管反流引起的最重要疾病之一。如果不进行治疗，该疾病将导致食管远端和贲门腺癌。短节段巴雷特黏膜的恶性风险非常高。因此，病变区域分割有助于专家做出治疗决策。在本文中，我们提出了一种结合模糊方法和主动模型的巴雷特黏膜分割方法。在本研究中，我们应用了三种方法进行特殊区域的分割和确定。对于整个疾病区域的分割，我们应用了基于混合模糊的水平集方法（LSM）。形态学算法用于确定胃食管交界处，并且我们通过应用Chan-Vase方法将巴雷特黏膜与破损区分开。由于边界模糊，模糊c均值和水平集方法无法分割这类医学图像。相比之下，本文中使用的具有相关方法的全自动混合方法以理想的精度分割了内窥镜图像中的化生区域。所提出的方法省略了需要操作员操作的手动期望聚类选择步骤。获得的结果使我们相信该方法适用于食管化生分割。
