Department Electronic Systems at Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Intervention Centre, Oslo University Hospital, Oslo NO-0027, Norway; Institute of Clinical Medicine, University of Oslo, and the Norwegian University of Science and Technology (NTNU), Norway.
Comput Med Imaging Graph. 2018 Nov;69:33-42. doi: 10.1016/j.compmedimag.2018.08.001. Epub 2018 Aug 22.
Polyps in the colon can potentially become malignant cancer tissues where early detection and removal lead to high survival rate. Certain types of polyps can be difficult to detect even for highly trained physicians. Inspired by aforementioned problem our study aims to improve the human detection performance by developing an automatic polyp screening framework as a decision support tool. We use a small image patch based combined feature method. Features include shape and color information and are extracted using histogram of oriented gradient and hue histogram methods. Dictionary learning based training is used to learn features and final feature vector is formed using sparse coding. For classification, we use patch image classification based on linear support vector machine and whole image thresholding. The proposed framework is evaluated using three public polyp databases. Our experimental results show that the proposed scheme successfully classified polyps and normal images with over 95% of classification accuracy, sensitivity, specificity and precision. In addition, we compare performance of the proposed scheme with conventional feature based methods and the convolutional neural network (CNN) based deep learning approach which is the state of the art technique in many image classification applications.
结肠息肉可能会变成恶性癌组织，早期发现和切除可提高生存率。即使是高训练有素的医生，某些类型的息肉也很难检测到。受上述问题的启发，我们的研究旨在通过开发自动息肉筛查框架作为决策支持工具来提高人类检测性能。我们使用基于小图像补丁的组合特征方法。特征包括形状和颜色信息，使用方向梯度直方图和色调直方图方法提取。基于字典学习的训练用于学习特征，最终特征向量使用稀疏编码形成。对于分类，我们使用基于线性支持向量机的补丁图像分类和整个图像阈值。使用三个公共息肉数据库评估提出的框架。我们的实验结果表明，所提出的方案成功地对息肉和正常图像进行了分类，分类准确率、灵敏度、特异性和精度均超过 95%。此外，我们还将所提出的方案与基于传统特征的方法和基于卷积神经网络（CNN）的深度学习方法进行了比较，后者是许多图像分类应用中的最新技术。
