Background and objective

Non-rigid image registration is a well-established method for estimating cardiac motion on 3D echocardiographic images. However, such images have relatively poor spatio-temporal resolution making registration challenging. Some of the main challenges are extracting features relevant to the registration problem and defining a suitable geometrical transformation to be applied. The latter can be tackled using a fuzzy inference system considering its potential in transformation modeling. From this point of view, feature-based image registration can be considered an identification problem in which the transformation parameters are computed through an optimization process. This study, thus, aims to estimate cardiac motion on 3D echocardiographic images based on feature-based non-rigid image registration through sets of modified fuzzy rules.

Methods

The 3D volume features were extracted with the popular scale-invariant feature transform (SIFT) descriptors in 3D space. Sets of fuzzy rules were generated according to the extracted features to register every two consecutive frames. Finally, some supplementary rules modified the registration rule for estimating cardiac motion.

Results

Applying the fuzzy feature-based inference system on the STRAUS synthetic database showed the proposed method to be competitive with other well-established registration algorithms in terms of tracking error and accuracy of strain estimates. The proposed algorithm yielded a tracking error of 1 mm and a relative circumferential strain error of 0.82±4.69%. In addition, the potential of the proposed algorithm for clinical applications was confirmed by evaluating its performance on an in-vivo database called CETUS.

Conclusion

This paper proposes a novel registration method based on fuzzy logic which was shown to enable tracking complex cardiac deformations in 3D echocardiographic images with high accuracy.

中文翻译：

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使用改进的基于模糊特征的推理系统进行3D心脏运动估计的非刚性图像配准

背景和目标

非刚性图像配准是在3D超声心动图图像上估计心脏运动的公认方法。然而，这样的图像具有相对差的时空分辨率，使得配准具有挑战性。一些主要挑战是提取与配准问题相关的特征并定义要应用的合适几何变换。考虑到其在转换建模中的潜力，可以使用模糊推理系统来解决后者。从这一观点出发，基于特征的图像配准可以被认为是识别问题，其中通过优化过程来计算变换参数。因此，本研究旨在通过修改后的模糊规则集，基于基于特征的非刚性图像配准，来估计3D超声心动图图像上的心脏运动。

方法

使用3D空间中流行的尺度不变特征变换（SIFT）描述符提取3D体积特征。根据提取的特征生成模糊规则集，以每两个连续的帧进行配准。最后，一些补充规则修改了用于估计心脏运动的注册规则。

结果

在STRAUS综合数据库上应用基于模糊特征的推理系统表明，该方法在跟踪误差和应变估计的准确性方面与其他成熟的配准算法具有竞争优势。该算法产生的跟踪误差为1 mm，相对圆周应变误差为0.82±4.69％。此外，通过在称为CETUS的体内数据库中评估其性能，证实了所提出算法在临床上的潜力。

结论

本文提出了一种基于模糊逻辑的新颖的配准方法，该方法被证明可以高精度地跟踪3D超声心动图图像中的复杂心脏变形。