肌骨超声图像特征检测及拼接  

Feature detection algorithm of musculoskeletal ultrasound image and its application of image stitching

作　　者：颜焕欢 张培镇 王伊侬 李璇[3] 阳维[1,2] 王青[1,2] Yan Huanhuan;Zhang Peizhen;Wang Yinong;Li Xuan;Yang Wei;Wang Qing(School of Biomedical Engineering,Southern Medical University,Guangzhou 510515,China;Guangdong Provincial Key Laboratory of Medical Image Processing,Southern Medical University,Guangzhou 510515,China;Nanfang Hospital,Southern Medical University,Guangzhou 510515,China)

机构地区：[1]南方医科大学生物医学工程学院,广州510515 [2]广东省医学图像处理重点实验室南方医科大学,广州510515 [3]南方医科大学南方医院,广州510515

出　　处：《中国图象图形学报》2020年第5期1032-1042,共11页Journal of Image and Graphics

基　　金：国家自然科学基金项目(81371560);广东省省级科技计划基金项目(2016A020216017)。

摘　　要：目的 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 elementaryfeatures (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 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 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2~5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 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 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 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 FASTSIFT. Then,based on the obtained feature points and their corresponding feature point descriptors,the nearest neighbor distance ratio method is applied to achieve rough f

关 键 词：肌骨超声图像 特征检测 特征匹配 图像配准 图像拼接 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]