Li Baopu, Meng Max Q H, Xu Lisheng
Department of Electronic Engineering at The Chinese University of Hong Kong, Hong Kong SAR, China.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:3731-4. doi: 10.1109/IEMBS.2009.5334875.
Wireless capsule endoscopy (WCE) has been gradually employed in hospitals because it can directly view the entire small bowel of a human body for the first time. However, a troublesome problem related to this new technology is that too many images produced by WCE will take a lot of efforts for doctors to inspect. In this paper, we propose a comparative study of shape features aiming for intestinal polyp detection for WCE images. As polyps exhibit strong shape characteristics, also a powerful clue used by physicians, we investigate two kinds of shape features, MEPG-7 region-based shape descriptor and Zernike moments, in our study. With multi-layer perceptron neural network as the classifier, experiments on our present image data show that it is promising to employ both Zernike moments and MEPG-7 region-based shape descriptor as the shape features to recognize the intestinal polyp regions, and a better performance is obtained by the Zernike moments based shape features.
无线胶囊内窥镜检查（WCE）已逐渐在医院中得到应用，因为它首次能够直接观察人体的整个小肠。然而，与这项新技术相关的一个棘手问题是，WCE产生的图像过多，医生检查起来会花费大量精力。在本文中，我们针对WCE图像中的肠息肉检测提出了一项形状特征的比较研究。由于息肉具有很强的形状特征，这也是医生使用的一个有力线索，因此我们在研究中考察了两种形状特征，即基于MPEG - 7区域的形状描述符和泽尼克矩。以多层感知器神经网络作为分类器，对我们目前的图像数据进行的实验表明，将泽尼克矩和基于MPEG - 7区域的形状描述符都用作形状特征来识别肠息肉区域是有前景的，并且基于泽尼克矩的形状特征能获得更好的性能。
