首页> 外文期刊>International Journal of Wavelets, Multiresolution and Information Processing >SCALE-SPACE SEGMENT GROWING FOR HIERARCHICAL DETECTION OF BILIARY TREE STRUCYURE
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SCALE-SPACE SEGMENT GROWING FOR HIERARCHICAL DETECTION OF BILIARY TREE STRUCYURE

机译:规模空间段的增长,对树形结构进行分层检测

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摘要

Liver diseases are a common medical problem, especially amongst the population of developing countries. Magnetic Resonance Cholangio Pancreatography (MRCP) has become the popular non-invasive, non-ionizing examination for analysis of the hepatobiliary structure in the liver. Unfortunately, conventional 2D MRCP images can be difficult to analyze for biliary tree anomalies, especially with volume effect, artefacts and noise present in these images, whilst good 3D MRI systems are costly for less affluent nations. This paper proposes a scale-space multi-resolution approach to a segment-based implementation of the popular region growing algorithm, to identify the hierarchical structure of the biliary tree in conventional 2D MRCP images. Results obtained are promising in aiding automatic processing of these images to assist medical practitioners in analyzing the biliary tract more efficiently. Application of the algorithm may be extended for telemedicine.
机译:肝病是一个普遍的医学问题,尤其是在发展中国家的人群中。磁共振胆管造影(MRCP)已成为分析肝脏中肝胆结构的一种流行的非侵入性,非电离检查方法。不幸的是,传统的2D MRCP图像可能很难分析胆道畸形,尤其是在这些图像中存在体积效应,伪影和噪声的情况下,而好的3D MRI系统对于富裕国家来说价格昂贵。本文提出了一种基于尺度空间的多分辨率方法,用于基于片段的流行区域增长算法的实现,以识别常规2D MRCP图像中胆道树的分层结构。获得的结果有望帮助这些图像的自动处理,以帮助医生更有效地分析胆道。该算法的应用可以扩展到远程医疗。

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