Industrial Electronics Department, University of Minho, Portugal.
Biomed Eng Online. 2012 Jan 11;11:3. doi: 10.1186/1475-925X-11-3.
Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity.
The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis.
The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice.
无线胶囊内窥镜作为一种创新性的非侵入性诊断技术，已被引入用于评估胃肠道，可到达传统内窥镜无法到达的部位。然而，这项技术的输出是长达 8 小时的视频，专家医生对其进行分析非常耗时。因此，开发一种计算机辅助诊断工具，帮助医生更快、更准确地评估胶囊内镜检查，是一项重要的技术挑战和绝佳的经济机会。
本文提出的用于编码纹理信息的特征集基于从共生矩阵中提取的二阶纹理测度的统计建模。为了处理二阶纹理测度的联合和边缘非高斯性，使用了更高阶的矩。这些统计矩取自二维彩色尺度特征空间，其中考虑了两个不同的尺度。从通过仅选择三个颜色通道的所选尺度的逆小波变换合成的图像的小波变换中包含的协方差矩阵中计算纹理测度的二阶和更高阶矩。通过主成分分析降低数据的维数。
然后，将所提出的纹理特征用作基于人工神经网络的分类器的输入。在真实数据上实现了 93.1%特异性和 93.9%敏感性的分类性能。这些有希望的结果为在计算机辅助诊断系统中应用该算法以帮助医生进行临床实践开辟了道路。
