College of Computer Science, Chongqing University, Chongqing 400044, China.
College of Computer Science, Chongqing University, Chongqing 400044, China; Key Laboratory of Dependable Service Computing in Cyber Physical Society, Chongqing University, Ministry of Education, China.
Comput Methods Programs Biomed. 2017 Jul;145:53-66. doi: 10.1016/j.cmpb.2017.04.010. Epub 2017 Apr 13.
Magnification endoscopy with narrow-band imaging (ME-NBI) has become a feasible tool for detecting diseases within the human gastrointestinal tract, and is more applied by physicians to search for pathological abnormalities with gastric cancer such as precancerous lesions, early gastric cancer and advanced cancer. In order to improve the reliability of diseases detection, there is a need for applying or proposing computer-assisted methodologies to efficiently analyze and process ME-NBI images. However, traditional computer vision methodologies, mainly segmentation, do not express well to the specific visual characteristics of NBI scenario.
In this paper, two energy functional items based on specific visual characteristics of ME-NBI images have been integrated in the framework of Chan-Vese model to construct the Hue-texture-embedded model. On the one hand, a global hue energy functional was proposed representing a global color information extracted in H channel (HSI color space). On the other hand, a texture energy was put forward presenting local microvascular textures extracted by the PIF of adaptive threshold in S channel.
The results of our model have been compared with Chan-Vese model and manual annotations marked by physicians using F-measure and false positive rate. The value of average F-measure and FPR was 0.61 and 0.16 achieved through the Hue-texture-embedded region-based model. And the C-V model achieved the average F-measure and FPR value of 0.52 and 0.32, respectively. Experiments showed that the Hue-texture-embedded region-based outperforms Chan-Vese model in terms of efficiency, universality and lesion detection.
Better segmentation results are acquired by the Hue-texture-embedded region-based model compared with the traditional region-based active contour in these five cases: chronic gastritis, intestinal metaplasia and atrophy, low grade neoplasia, high grade neoplasia and early gastric cancer. In the future, we are planning to expand the universality of our proposed methodology to segment other lesions such as intramucosal cancer etc. As long as these issues are solved, we can proceed with the classification of clinically relevant diseases in ME-NBI images to implement a fully automatic computer-assisted diagnosis system.
窄带成像放大内镜（ME-NBI）已成为一种检测人体胃肠道疾病的可行工具，医生更倾向于用其来寻找胃癌等疾病的病理异常，如癌前病变、早期胃癌和晚期癌症。为了提高疾病检测的可靠性，需要应用或提出计算机辅助方法来有效地分析和处理 ME-NBI 图像。然而，传统的计算机视觉方法，主要是分割，不能很好地表达 NBI 场景的特定视觉特征。
在本文中，基于 ME-NBI 图像的特定视觉特征，在 Chan-Vese 模型的框架中集成了两个能量函数项，构建了色调-纹理嵌入模型。一方面，提出了一种全局色调能量函数，用于表示从 H 通道（HSI 颜色空间）提取的全局颜色信息。另一方面，提出了一种纹理能量，用于表示通过自适应阈值的 PIF 在 S 通道提取的局部微血管纹理。
使用 F-measure 和假阳性率将我们的模型结果与 Chan-Vese 模型和医生手动标记进行了比较。通过基于色调-纹理的区域模型，平均 F-measure 和 FPR 的值分别为 0.61 和 0.16。而 C-V 模型的平均 F-measure 和 FPR 值分别为 0.52 和 0.32。实验表明，基于色调-纹理的区域模型在效率、通用性和病变检测方面优于 Chan-Vese 模型。
与传统的基于区域的主动轮廓相比，基于色调-纹理的区域模型在这五个病例中获得了更好的分割结果：慢性胃炎、肠上皮化生和萎缩、低级别肿瘤、高级别肿瘤和早期胃癌。在未来，我们计划扩大我们提出的方法的通用性，以分割其他病变，如黏膜内癌等。只要解决了这些问题，我们就可以对 ME-NBI 图像中的临床相关疾病进行分类，实现全自动计算机辅助诊断系统。
