基于全局信息的鲁棒活动轮廓的目标边界检测方法

R. Kashyap
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引用次数: 19

摘要

恢复性应用已转向医疗保健行业,各种治疗应用需要对医学图像进行合法的分割以进行准确的确定。这些应用保证了传统方法对医学图像的惊人分割,这些方法影响了分割的准确性,分割效果更好。在该方法中,横截面玻尔兹曼方法取代了偏微分方程,加快了求解速度。本文提出了一种增强的主动轮廓方法,该方法与局部和全局能量项相协调,局部项强制拉动形式并将其限制在物体边界上,确定了不受快速准备、机械化、精确CT图像部分不变性等限制的显著优势。因此,整体能量拟合项在物体边界分离处驱动形式的发展;它推断出有利可图的兴趣点,而不是简单地利用快速的处理,计算机化和正确的恢复图片部分。该方法在主观上和定量上都优于其他方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Object boundary detection through robust active contour based method with global information
Restorative applications have turned to the healthcare industry various therapeutic applications require legitimate segmentation of medical images for an exact determination. These applications guarantee astounding segmentation of medical images using traditional methods these methods influences the segmentation exactness, better segmentation. In the proposed method, cross section Boltzmann method replaces the partial differential equation that speed up the process. Here an enhanced active contour method that coordinates with both local and global energy terms, local term compels to pull the form and limit it to object boundary, determines significant advantages not restricted to, quick preparing, mechanisation, invariance of precise CT image portions. Thus, the global energy fitting term drives the development of form at a separation of the object boundary; it infers profitable points of interest not stuck simply utilising speedy process, computerisation and right restorative picture portions. The proposed method performs better subjectively and quantitatively contrasted with other methods.
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