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Polyps's region of interest detection in colonoscopy images by using clustering segmentation and region growing

机译:通过聚类分割和区域生长在结肠镜检查图像中检测息肉的感兴趣区域

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摘要

In this paper we propose a novel method to automatically detect the polyp's ROI (Region Of Interest) based on a Gaussian Mixture Model, Esperance Maximization algorithm segmentation, Hough transform and region growing. Our main objective through this work is to help in the early detection and precise analyze for the polyps diagnosis, which is a huge apport for the health care. The experimental results show that the proposed method can achieve 83.15% accuracy and 98% sensitivity on our data set.
机译:在本文中,我们提出了一种基于高斯混合模型,Essperance最大化算法分割,Hough变换和区域增长的自动检测息肉ROI(感兴趣区域)的新方法。通过这项工作,我们的主要目标是帮助您进行息肉诊断的早期检测和精确分析,这对医疗保健而言是一个巨大的优势。实验结果表明,该方法在我们的数据集上可以达到83.15%的精度和98%的灵敏度。

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