The Chinese University of Hong Kong, Shatin, Hong Kong; Johns Hopkins University (JHU), Baltimore, MD 21218, USA.
Johns Hopkins University (JHU), Baltimore, MD 21218, USA.
Comput Med Imaging Graph. 2015 Apr;41:108-16. doi: 10.1016/j.compmedimag.2014.05.011. Epub 2014 Jun 10.
We present a general framework for analysis of wireless capsule endoscopy (CE) studies. The current available workstations provide a time-consuming and labor-intense work-flow for clinicians which requires the inspection of the full-length video. The development of a computer-aided diagnosis (CAD) CE workstation will have a great potential to reduce the diagnostic time and improve the accuracy of assessment. We propose a general framework based on hidden Markov models (HMMs) for study synopsis that forms the computational engine of our CAD workstation. Color, edge and texture features are first extracted and analyzed by a Support Vector Machine classifier, and then encoded as the observations for the HMM, uniquely combining the temporal information during the assessment. Experiments were performed on 13 full-length CE studies, instead of selected images previously reported. The results (e.g. 0.933 accuracy with 0.933 recall for detection of polyps) show that our framework achieved promising performance for multiple classification. We also report the patient-level CAD assessment of complete CE studies for multiple abnormalities, and the patient-level validation demonstrates the effectiveness and robustness of our methods.
我们提出了一个用于分析无线胶囊内窥镜（CE）研究的通用框架。当前可用的工作站为临床医生提供了耗时且费力的工作流程，需要检查整个视频。开发计算机辅助诊断（CAD）CE 工作站将具有很大的潜力，可以减少诊断时间并提高评估的准确性。我们提出了一个基于隐马尔可夫模型（HMM）的研究综述通用框架，该框架构成了我们 CAD 工作站的计算引擎。首先通过支持向量机分类器提取和分析颜色、边缘和纹理特征，然后将其编码为 HMM 的观测值，在评估过程中独特地结合了时间信息。实验是在 13 个全长 CE 研究上进行的，而不是以前报告的选择图像。结果（例如，检测息肉的准确率为 0.933，召回率为 0.933）表明，我们的框架在多分类方面取得了有希望的性能。我们还报告了对多个异常的完整 CE 研究的患者级 CAD 评估，患者级验证证明了我们方法的有效性和鲁棒性。
