Dept. of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
Dept. of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
Comput Biol Med. 2018 Mar 1;94:41-54. doi: 10.1016/j.compbiomed.2017.12.014. Epub 2018 Jan 5.
Wireless capsule endoscopy (WCE) is capable of demonstrating the entire gastrointestinal tract at an expense of exhaustive reviewing process for detecting bleeding disorders. The main objective is to develop an automatic method for identifying the bleeding frames and zones from WCE video. Different statistical features are extracted from the overlapping spatial blocks of the preprocessed WCE image in a transformed color plane containing green to red pixel ratio. The unique idea of the proposed method is to first perform unsupervised clustering of different blocks for obtaining two clusters and then extract cluster based features (CBFs). Finally, a global feature consisting of the CBFs and differential CBF is used to detect bleeding frame via supervised classification. In order to handle continuous WCE video, a post-processing scheme is introduced utilizing the feature trends in neighboring frames. The CBF along with some morphological operations is employed to identify bleeding zones. Based on extensive experimentation on several WCE videos, it is found that the proposed method offers significantly better performance in comparison to some existing methods in terms of bleeding detection accuracy, sensitivity, specificity and precision in bleeding zone detection. It is found that the bleeding detection performance obtained by using the proposed CBF based global feature is better than the feature extracted from the non-clustered image. The proposed method can reduce the burden of physicians in investigating WCE video to detect bleeding frame and zone with a high level of accuracy.
无线胶囊内镜（WCE）能够展示整个胃肠道，但在检测出血性疾病时需要进行详尽的审查过程。主要目标是开发一种自动方法，从 WCE 视频中识别出血帧和区域。从预处理的 WCE 图像的重叠空间块中提取不同的统计特征，这些块位于包含绿到红像素比的变换颜色平面中。该方法的独特思想是首先对不同的块进行无监督聚类，以获得两个聚类，然后提取基于聚类的特征（CBF）。最后，使用包含 CBF 和差分 CBF 的全局特征通过有监督分类来检测出血帧。为了处理连续的 WCE 视频，引入了一种后处理方案，利用相邻帧中的特征趋势。利用 CBF 结合一些形态学操作来识别出血区域。通过对几个 WCE 视频进行广泛的实验，发现与一些现有的方法相比，该方法在出血检测准确性、灵敏度、特异性和出血区域检测精度方面具有显著的优势。研究发现，使用基于 CBF 的全局特征提取的特征在出血检测性能方面优于非聚类图像提取的特征。该方法可以减轻医生在调查 WCE 视频以检测出血帧和区域时的负担，具有较高的准确性。
