The significance of mitral valve (MV) treatment is increasing recently because of an aging population. The computer vision-based acquisition and quantification of the valve anatomy becomes helpful for surgical and intercessional planning. The right option of common treatment and implantation is pertinent for the most favorable results. Several studies reported that the decision support system (DSS) could offer decisions based on the virtual involvement planning and prediction models. Generally, the segmentation and classification of MV from the computed tomography (CT) images are highly complicated, owing to the variations in appearance and visibility. In this paper, an efficient automated DSS model is introduced using watershed segmentation with Xception model for the MV classification. It incorporates four modules: bilateral filtering (BF) based preprocessing, watershed segmentation, Xception based feature extraction and random forest (RF) classification. A watershed algorithm with channel separation is used to segment the MV images. The Xception model with random forest (RF) model is utilized for training and classifying images. A detailed simulation is performed on the CT images collected from hospitals. The presented WS-X model is tested and a comparative study is made with the relevant works to highlight its superior nature. The obtained results stressed out that the WS-X model is an appropriate model for the MV problem under various aspects.

最近，由于人口老龄化，二尖瓣（MV）治疗的重要性在增加。基于计算机视觉的瓣膜解剖结构的采集和量化对于外科手术和代祷计划很有帮助。正确的选择是共同的治疗和植入，以获得最有利的结果。几项研究报告说，决策支持系统（DSS）可以基于虚拟参与计划和预测模型提供决策。通常，由于外观和可见性的变化，从计算机断层扫描（CT）图像对MV进行分割和分类非常复杂。在本文中，使用分水岭分割和Xception模型引入了一种有效的自动化DSS模型，用于MV分类。它包含四个模块：基于双边过滤（BF）的预处理，分水岭分割，基于Xception的特征提取和随机森林（RF）分类。具有通道分离的分水岭算法用于分割MV图像。具有随机森林（RF）模型的Xception模型用于训练和分类图像。对从医院收集的CT图像进行了详细的模拟。对提出的WS-X模型进行了测试，并与相关工作进行了对比研究，以突出其优越性。获得的结果强调了WS-X模型在各个方面都是解决MV问题的合适模型。具有随机森林（RF）模型的Xception模型用于训练和分类图像。对从医院收集的CT图像进行了详细的模拟。对提出的WS-X模型进行了测试，并与相关工作进行了对比研究，以突出其优越性。获得的结果强调了WS-X模型在各个方面都是解决MV问题的合适模型。具有随机森林（RF）模型的Xception模型用于训练和分类图像。对从医院收集的CT图像进行了详细的模拟。对提出的WS-X模型进行了测试，并与相关工作进行了对比研究，以突出其优越性。获得的结果强调了WS-X模型在各个方面都是解决MV问题的合适模型。