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Analysis of adductors angle measurement in Hammersmith infant neurological examinations using mean shift segmentation and feature point based object tracking

机译:使用均值偏移分割和基于特征点的对象跟踪分析Hammersmith婴儿神经系统检查中的内收肌角度测量

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摘要

This paper presents image and video analysis based schemes to automate the process of adductors angle measurement which is carried out on infants as a part of Hammersmith Infant Neurological Examination (HINE). Image segmentation, thinning and feature point based object tracking are used for automating the analysis. Segmentation outputs are processed with a novel region merging algorithm. It is found that the refined segmentation outputs can successfully be used to extract features in the context of the application under consideration. Next, a heuristic based filtering algorithm is applied on the thinned structures for locating necessary points to measure adductors angle. A semi-automatic scheme based on the object tracking of a video has been proposed to minimize errors of the image based analysis. It is observed that the video-based analysis outperforms the image-based method. A fully automatic method has also been proposed and compared with the semi-automatic algorithm. The proposed methods have been tested with several videos recorded from hospitals and the results have been found to be satisfactory in the present context.
机译:本文介绍了基于图像和视频分析的方案,以自动化作为Hammersmith婴儿神经系统检查(HINE)一部分对婴儿进行的内收肌角度测量过程。图像分割,细化和基于特征点的对象跟踪用于自动分析。分割输出使用一种新颖的区域合并算法进行处理。可以发现,在考虑中的应用程序上下文中,经过改进的细分输出可以成功用于提取特征。接下来,在变薄的结构上应用基于启发式的滤波算法,以定位必要的点以测量内收角。已经提出了基于视频的对象跟踪的半自动方案,以最小化基于图像的分析的误差。可以观察到,基于视频的分析优于基于图像的方法。还提出了一种全自动方法,并将其与半自动算法进行了比较。所提议的方法已通过医院录制的多个视频进行了测试,结果在当前情况下令人满意。

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