Wireless Capsule Endoscopy Color Video Segmentation
Wireless Capsule Endoscopy Color Video Segmentation
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DOI：
10.1109/tmi.2008.926061
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发表时间：
2008-12-01
影响因子：
10.6
通讯作者：
Fisher, Mark
中科院分区：
文献类型：
--
作者：
Mackiewicz, Michal;Berens, Jeff;Fisher, Mark
本文描述了在无线胶囊式内窥镜（WCE）中使用彩色图像分析来自动区分食管、胃、小肠和结肠组织。WCE使用“药丸摄像头”技术从整个胃肠道恢复彩色视频图像。准确地审查和报告这些数据是考试的重要组成部分，但它是繁琐和耗时的。自动图像分析工具在支持临床医生和加快这一过程中发挥着重要作用。我们的方法首先将WCE图像划分为子图像，并拒绝组织不清晰可见的所有子图像。然后，我们创建一个特征向量相结合的颜色，纹理和整个图像和有效的子图像的运动信息。颜色特征来自色调饱和度直方图，压缩使用混合变换结合离散余弦变换和主成分分析。第二个特征结合了颜色和纹理信息，使用本地二进制模式。视频被分割成有意义的部分，使用支持向量或多变量高斯分类器的隐马尔可夫模型的框架内建立。我们目前的实验结果表明，这种方法的有效性。
This paper describes the use of color image analysis to automatically discriminate between oesophagus, stomach, small intestine, and colon tissue in wireless capsule endoscopy (WCE). WCE uses "pill-cam" technology to recover color video imagery from the entire gastrointestinal tract. Accurately reviewing and reporting this data is a vital part of the examination, but it is tedious and time consuming. Automatic image analysis tools play an important role in supporting the clinician and speeding up this process. Our approach first divides the WCE image into subimages and rejects all subimages in which tissue is not clearly visible. We then create a feature vector combining color, texture, and motion information of the entire image and valid subimages. Color features are derived from hue saturation histograms, compressed using a hybrid transform incorporating the discrete cosine transform and principal component analysis. A second feature combining color and texture information is derived using local binary patterns. The video is segmented into meaningful parts using support vector or multivariate Gaussian classifiers built within the framework of a hidden Markov model. We present experimental results that demonstrate the effectiveness of this method.