Towards a multimodal wireless video capsule for detection of colonic polyps as prevention of colorectal cancer
Towards a multimodal wireless video capsule for detection of colonic polyps as prevention of colorectal cancer
复制标题
开发用于检测结肠息肉以预防结直肠癌的多模式无线视频胶囊
DOI：
10.1109/bibe.2013.6701670
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
P. Marteau
中科院分区：
文献类型：
--
作者：
O. Romain;A. Histace;Juan Silva;J. Ayoub;B. Granado;A. Pinna;X. Dray;P. Marteau
无线胶囊式内窥镜（WCE）通常用于非侵入性胃肠道评价，包括息肉的识别。在本文中，一个新的多模态嵌入方法的息肉检测和分类在无线胶囊内窥镜图像的开发和测试。多模式无线胶囊使用2D和3D数据来识别可能的息肉，并基于3D几何特征提供息肉的癌变信息。使用简单的几何形状特征提取图像（2D）内可能的息肉，在第二步中，使用纹理特征用基于增强的方法评估候选感兴趣区域（ROI）。一旦已经执行了息肉的2D识别，则使用主动立体视觉系统从预选的ROI计算的3D参数来实现息肉的两类（“恶性”或“初发”）分类。在此阶段，使用支持向量机（SVM）分类器进行最终分类，并进行预诊断。新提出的基于2D-3D特征提取的多模态方法提高了WCE识别和分类息肉的能力：基于增强的息肉分类在由300个阳性样本和1200个阴性样本组成的数据库上表现出91%的灵敏度，95%的特异性和4.8%的误检率;考虑到3D性能，对大规模演示器进行了评估和测试，以在特定息肉数据库上进行体外实验。3D方法的性能实现了约95%的正确分类率（恶性或良性）。
Wireless capsule endoscopy (WCE) is commonly used for noninvasive gastrointestinal tract evaluation, including the identification of polyps. In this paper, a new multimodal embeddable method for polyp detection and classification in wireless capsule endoscopic images was developed and tested. The multimodal wireless capsule used both 2D and 3D data to identify possible polyps and to deliver cancerous information of the polyps based on 3D geometric features. Possible polyps within the image (2D) were extracted using simple geometric shape features and, in a second step, the candidate regions of interest (ROI) were evaluated with a boosting-based method using textural features. Once the 2D identification of polyps has been performed, the two-class (“malignant” or “begnin”) classification of the polyps is achieved using the 3D parameters computed from the preselected ROI using an active stereo vision system. At this stage, a Support Vector Machine (SVM) classifier is used to proceed to the final classification and to make possible a pre diagnosis. The new proposed multimodal approach based on 2D-3D feature extraction improves WCE capabilities to identify and classify polyps: The boosting-based polyp classification demonstrated a sensitivity of 91%, a specificity of 95% and a false detection rate of 4.8% on a database composed of 300 hundred positive examples and 1200 negative ones; Considering the 3D performance, a large scale demonstrator was evaluated and tested to perform in vitro experiments on an ad hoc polyp database. The performance of the 3D approach achieved a correct classification rate (malignant or benin) of approximately 95%.