基于联合蛇和地图集的足底热图像分割

Asma Bougrine, R. Harba, R. Canals, R. Lédée, M. Jabloun
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引用次数: 13

摘要

本研究的目的是提出一种新的针对足底热图像的关节分割方法。该方法基于一种改进的主动轮廓法(Snake),该方法在Snake能量函数中增加了一个先验形状信息,即足底轮廓图谱。这个术语通过计算Snake曲线和足底足表面的地图集曲线之间的曲率差,在变形过程中引导Snake到达目标轮廓。利用50张足底热图像数据库对该方法进行了验证。结果表明,该方法优于经典的Snake方法和其他7种新方法。比较采用两个评价指标,均方根误差(RMSE)和骰子相似系数(DSC)。与地面真实值相比,该方法的平均RMSE为6像素,DSC得分为93%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A joint snake and atlas-based segmentation of plantar foot thermal images
The aim of the present study is to propose a new joint segmentation method dedicated to plantar foot thermal images. The proposed method is based on a modified active contour method (Snake) that includes a prior shape information, namely an atlas of the plantar foot contour, as an extra term in the Snake energy function. This term guides the Snake to the targeted contours during the deformation process, by calculating a curvature difference between the Snake curve and the atlas curve of the plantar foot surface. The proposed method was validated using a database of 50 plantar foot thermal images. Results showed the proposed method to outperform the classical Snake method and seven other recent methods. The comparison was done using two evaluation metrics, the root-mean-square error (RMSE) and the dice similarity coefficient (DSC). When compared to ground truth, the best average RMSE of 6 pixels and DSC score of 93% were obtained using the proposed method.
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