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Motion Estimation Using the Firefly Algorithm in Ultrasonic Image Sequence of Soft Tissue

机译:基于萤火虫算法的软组织超声图像序列运动估计

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摘要

Ultrasonic image sequence of the soft tissue is widely used in disease diagnosis; however, the speckle noises usually influenced the image quality. These images usually have a low signal-to-noise ratio presentation. The phenomenon gives rise to traditional motion estimation algorithms that are not suitable to measure the motion vectors. In this paper, a new motion estimation algorithm is developed for assessing the velocity field of soft tissue in a sequence of ultrasonic B-mode images. The proposed iterative firefly algorithm (IFA) searches for few candidate points to obtain the optimal motion vector, and then compares it to the traditional iterative full search algorithm (IFSA) via a series of experiments of in vivo ultrasonic image sequences. The experimental results show that the IFA can assess the vector with better efficiency and almost equal estimation quality compared to the traditional IFSA method.
机译:软组织的超声图像序列被广泛用于疾病诊断。但是,斑点噪声通常会影响图像质量。这些图像通常具有较低的信噪比显示。这种现象引起了不适合测量运动矢量的传统运动估计算法。本文提出了一种新的运动估计算法,用于评估一系列超声B型图像中软组织的速度场。提出的迭代萤火虫算法(IFA)搜索少量候选点以获得最佳运动矢量,然后通过一系列体内超声图像序列实验将其与传统的迭代全搜索算法(IFSA)进行比较。实验结果表明,与传统的IFSA方法相比,IFA可以更好地评估矢量,并且评估质量几乎相等。

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