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Real-time automatic extraction of lumen region and boundary from endoscopic images.

机译:从内窥镜图像实时自动提取管腔区域和边界。

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摘要

A new approach to the automatic extraction of the lumen region and its boundary for gastrointestinal (GI) endoscopic images is presented. First, a quasi region of interest, the darker regions of the image, is segmented using a region splitting scheme termed progressive thresholding. The centre of mass of this segmented region acts as a seed for further processing. Then the lumen region is obtained using a region growing technique called the integrated neighbourhood search (INS). A new quad structure based technique is introduced to enhance the INS speed significantly. A back projection algorithm is suggested to optimise the search for pixels belonging to the lumen region and boundary. A boundary-thinning algorithm is also proposed to remove the redundant pixels from the lumen boundary and to generate a connected single pixel width boundary. The proposed approach does not need a priori knowledge about the image characteristics. The experimental results indicate that the proposed technique enhances the speed of conventional INS by 45.5% to 28.6% based on the lumen size varying from 22,709 pixels to 4947 pixels. The main advantage of the proposed technique is its high-speed response that facilitates real-time analysis of endoscopic images.
机译:提出了一种自动提取胃肠道(GI)内窥镜图像内腔区域及其边界的新方法。首先,使用称为渐进阈值的区域划分方案对拟定的感兴趣区域(图像的较暗区域)进行分割。该分割区域的质心充当进一步处理的种子。然后,使用称为集成邻域搜索(INS)的区域生长技术获得管腔区域。引入了一种基于四边形结构的新技术来显着提高INS速度。建议使用反投影算法来优化对属于管腔区域和边界的像素的搜索。还提出了边界稀疏算法,以从流明边界中去除冗余像素并生成连接的单个像素宽度边界。所提出的方法不需要关于图像特征的先验知识。实验结果表明,基于从22,709像素到4947像素的流明大小,所提出的技术将常规INS的速度提高了45.5%至28.6%。所提出技术的主要优点是其高速响应,有助于实时分析内窥镜图像。

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