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收稿：2019-07-19，
修回：2019-9-23，
录用：2019-9-30，
纸质出版：2020-05-16
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目的
2
肌骨超声宽景图像易出现解剖结构错位、断裂等现象，其成像算法中的特征检测影响宽景图像的质量，也是超声图像配准、分析等算法的关键步骤，但目前仍未有相关研究明确指出适合提取肌骨超声图像特征点的算法。本文利用结合SIFT（scale invariant feature transform）描述子的FAST（features from accelerated segment test）算法以及SIFT、SURF（speeded-up robust features）、ORB（oriented FAST and rotated binary robust independent elementary features（BRIEF））算法对肌骨超声图像序列进行图像拼接，并对各算法的性能进行比较评估，为肌骨超声图像配准、宽景成像提供可参考的特征检测解决方案。
方法
2
采集5组正常股四头肌的超声图像序列，每组再采样10幅图像。利用经典的图像拼接算法进行肌骨图像的特征检测以及图像拼接。分别利用上述4种算法提取肌骨超声图像的特征点；对特征点进行特征匹配，估算出图像间的形变矩阵；对所有待拼接的图像进行坐标变换以及融合处理，得到拼接全景图，并在特征检测性能、特征匹配性能、图像配准性能以及拼接效果等方面对4种算法进行评估比较。
结果
2
实验结果表明，与SIFT、SURF、ORB算法相比，FAST-SIFT算法所提取的特征点分布更均匀，可以检测到大部分肌纤维的端点，且特征点检测时间最短，约4 ms，其平均匹配对数最多，是其他特征检测算法的25倍，其互信息和归一化互相关系数均值分别为1.016和0.748，均高于其他3种特征检测算法，表明其图像配准精度更高。且FAST-SIFT算法的图像拼接效果更好，没有明显的解剖结构错位、断裂、拼接不连贯等现象。
结论
2
与SIFT、SURF、ORB算法相比，FAST-SIFT算法是更适合提取肌骨超声图像特征点的特征检测算法，在图像配准精度等方面都具有一定的优势。
Objective
2
Musculoskeletal ultrasound (MSKUS) is an imaging diagnosis method commonly applied in the diagnosis and treatment of musculoskeletal diseases. The feature detection of MSKUS image plays an important role in image registration
image analysis of MSKUS images
and extended field-of-view ultrasound imaging
requiring extraction of the effective feature points. However
the contrast of the ultrasound image is low
and speckle noise and image artifacts are presented in the MSKUS images. These limitations negatively affect the extraction of the feature points of MSKUS image. Consequently
the accuracy of image registration and the quality of image stitching are affected. This condition may lead to misalignment and fracture of anatomical structure on the MSKUS panoramic image. An algorithm suitable for detecting feature points of MSKUS images has not been clearly determined. The objectives of this study are to evaluate the performance of the four local feature detection algorithms on stitching MSKUS sequence images
including scale invariant feature transform (SIFT)
speeded-up robust features (SURF)
oriented FAST and rotated binary robust independent elementary features(ORB)
and features from accelerated segment test (FAST) combined with SIFT descriptor
and to provide a basis and reference solution of feature detection for MSKUS image registration and extended field-of-view ultrasound imaging in future research.
Method
2
Ultrasound image sequences of the quadriceps muscles in five normal human subjects are collected. From the image sequence of each subject
10 images are resampled every five frames for image feature detection and image stitching. The classical image stitching method proposed by Brown is adopted in this study
which includes the following three main steps. First
the feature points of the MSKUS image are extracted by SIFT
SURF
ORB
and FAST-SIFT. Then
based on the obtained feature points and their corresponding feature point descriptors
the nearest neighbor distance ratio method is applied to achieve rough feature matching
and the random sample consensus (RANSAC) algorithm is used to realize fine feature matching. The projection transformation matrix is taken as the basic model to estimate the optimal deformation matrix between the two images. Finally
the deformation matrix between the two images is used to obtain the internal parameters of the camera and the external parameters of the camera. These camera parameters can be used to transform the 10 images into the coordinate system of the reference image. After coordinate transformation
all the 10 images are stitched together. Then
the MSKUS panorama is post-processed using the maximum flow minimum cut algorithm to find the unwell-stitched overlapped area. The multiband fusion is used to reduce the artificial and rough seam zones. The MSKUS panorama is eventually obtained. To evaluate the performance of the four algorithms SIFT
SURF
ORB algorithms
and FAST-SIFT
detection of feature points
feature matching
image registration
and image stitching are assessed.
Result
2
The experimental results show that compared with SIFT
SURF
and ORB algorithms
FAST-SIFT is able to extract more uniform distribution feature points and detect most of the end points of the muscle fibers. Furthermore
the detection time of feature points by using FAST-SIFT is much shorter of approximately 4 ms.The average number of matching points in FAST-SIFT is the largest
which is 2~5 times as many as other feature detection algorithms. FAST-SIFT algorithm has the highest correct rate of feature matching. The mean value of mutual information and normalized cross-correlation coefficient of FAST-SIFT are 1.016 and 0.748
respectively
which are higher than that of the other three feature detection algorithms. Result indicates high accuracy of image registration. Moreover
the stitched panorama of MSKUS by using the FAST-SIFT algorithm shows good image stitching
that is
no obvious misalignment and fracture of anatomical structure are found.
Conclusion
2
Compared with SIFT
SURF
and ORB
FAST-SIFT algorithm is more suitable to extract the feature points of MSKUS image. It has advantages in the distribution of feature points
the detection time of feature points
the average number of matching points
the correct rate of feature matching
image registration accuracy
and image stitching result.
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