Medical & Biological Engineering & Computing ( IF 2.6 ) Pub Date : 2021-04-10 , DOI: 10.1007/s11517-021-02352-8 S Rathnamala 1 , S Jenicka 2
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Wireless capsule endoscopy is the commonly employed modality in the treatment of gastrointestinal tract pathologies. However, the time taken for interpretation of these images is very high due to the large volume of images generated. Automated detection of disorders with these images can facilitate faster clinical interventions. In this paper, we propose an automated system based on Gaussian mixture model superpixels for bleeding detection and segmentation of candidate regions. The proposed system is realized with a classic binary support vector machine classifier trained with seven features including color and texture attributes extracted from the Gaussian mixture model superpixels of the WCE images. On detection of bleeding images, bleeding regions are segmented from them, by incrementally grouping the superpixels based on deltaE color differences. Tested with standard datasets, this system exhibits best performance compared to the state-of-the-art approaches with respect to classification accuracy, feature selection, computational time, and segmentation accuracy. The proposed system achieves 99.88% accuracy, 99.83% sensitivity, and 100% specificity signifying the effectiveness of the proposed system in bleeding detection with very few classification errors.
Graphical abstract
中文翻译：
基于高斯混合模型超像素颜色特征提取的无线胶囊内窥镜图像自动出血检测
无线胶囊内窥镜检查是治疗胃肠道疾病的常用方式。然而，由于生成的大量图像，解释这些图像所花费的时间非常长。使用这些图像自动检测疾病可以促进更快的临床干预。在本文中，我们提出了一种基于高斯混合模型超像素的自动化系统，用于候选区域的出血检测和分割。所提出的系统是通过经典的二元支持向量机分类器实现的，该分类器使用七个特征进行训练，包括从 WCE 图像的高斯混合模型超像素中提取的颜色和纹理属性。在检测出血图像时，从中分割出血区域，通过基于 deltaE 颜色差异对超像素进行增量分组。使用标准数据集进行测试，该系统在分类精度、特征选择、计算时间和分割精度方面与最先进的方法相比表现出最佳性能。所提出的系统实现了 99.88% 的准确度、99.83% 的灵敏度和 100% 的特异性，这表明所提出的系统在出血检测中的有效性，并且分类错误很少。
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