Institute of Precise Engineering and Intelligent Microsystems, Shanghai Jiaotong University, Shanghai, 200240, China.
Institute of Precise Engineering and Intelligent Microsystems, Shanghai Jiaotong University, Shanghai, 200240, China.
Comput Biol Med. 2016 Mar 1;70:131-138. doi: 10.1016/j.compbiomed.2016.01.021. Epub 2016 Jan 25.
Wireless capsule endoscopy (WCE) has been a revolutionary technique to noninvasively inspect gastrointestinal (GI) tract diseases, especially small bowel tumor. However, it is a tedious task for physicians to examine captured images. To develop a computer-aid diagnosis tool for relieving the huge burden of physicians, the intestinal video data from 89 clinical patients with the indications of potential tumors was analyzed. Out of the 89 patients, 15(16.8%) were diagnosed with small bowel tumor. A novel set of textural features that integrate multi-scale curvelet and fractal technology were proposed to distinguish normal images from tumor images. The second order textural descriptors as well as higher order moments between different color channels were computed from images synthesized by the inverse curvelet transform of the selected scales. Then, a classification approach based on support vector machine (SVM) and genetic algorithm (GA) was further employed to select the optimal feature set and classify the real small bowel images. Extensive comparison experiments validate that the proposed automatic diagnosis scheme achieves a promising tumor classification performance of 97.8% sensitivity and 96.7% specificity in the selected images from our clinical data.
无线胶囊内窥镜 (WCE) 是一种革命性的技术，可以非侵入性地检查胃肠道 (GI) 疾病，特别是小肠肿瘤。然而，对于医生来说，检查捕获的图像是一项繁琐的任务。为了开发一种计算机辅助诊断工具来减轻医生的巨大负担，对 89 名有潜在肿瘤指征的临床患者的肠道视频数据进行了分析。在这 89 名患者中，有 15 名（16.8%）被诊断为小肠肿瘤。提出了一组新的纹理特征，将多尺度curvelet 和分形技术集成在一起，以区分正常图像和肿瘤图像。从所选尺度的逆curvelet 变换合成的图像中计算出二阶纹理描述符以及不同颜色通道之间的高阶矩。然后，进一步采用基于支持向量机 (SVM) 和遗传算法 (GA) 的分类方法来选择最佳特征集并对真实的小肠图像进行分类。广泛的比较实验验证了所提出的自动诊断方案在我们的临床数据中选择的图像中实现了有希望的肿瘤分类性能，灵敏度为 97.8%，特异性为 96.7%。
