目的 血管内超声（IVUS）图像动脉壁边界分割不仅对血管壁和斑块特征的定量分析至关重要，而且对血管弹性定性分析和重建动脉3维模型也是必需的。针对IVUS图像传统分割方法建模复杂、运算量大且需分别设计算法串行提取内膜和外膜的缺点，本文提出基于极值区域检测的IVUS图像并行分割方法。方法 本文方法包含极值区域检测、极值区域筛选以及轮廓拟合3部分。对单帧IVUS图像提取极值区域，经面积筛选后得到候选区域，并将区域的局部二值模式（LBP）特征、灰度差异和边缘周长的乘积作为筛选矢量在候选区域中提取代表管腔和介质的两个极值区域，并进行轮廓的椭圆拟合化，完成分割。结果 在包含326幅20 MHz的IVUS（intravascular ultrasound）B模式图像的标准公开数据集上，定性展示极值区域轮廓和椭圆拟合轮廓，并与专家手动绘制的结果进行对比；然后使用DC（dice coefficient）、JI（jaccard index）、PAD（percentage of area difference）指标以及HD（hausdorff distance）对本文算法做鲁棒性测试和泛化测试，实验中内膜各指标值分别为0.94±0.02，0.90±0.04，0.05±0.05，0.28±0.14 mm，外膜各指标值分别为0.91±0.07，0.87±0.11，0.11±0.11，0.41±0.31 mm，与相关文献的定量对比实验结果表明本文算法提取的内外膜性能均有所提高。此外，本文方法在临床数据集上的测试效果也很好，与专家手动描绘十分接近。结论 结合极值区域检测的IVUS图像并行分割，算法在精度和鲁棒性方面均得到了改善。

Objective Intravascular ultrasound (IVUS) image segmentation of arterial wall boundaries are essential not only for the quantitative analysis of the characteristics of vascular walls and plaques but also for the qualitative analysis of vascular elasticity and the reconstruction of the 3D model of arteries. The importance lies in the following:1)IVUS image segmentation is the basis for follow-up work, such as plaque extraction and recognition, vessel wall elasticity analysis, and image registration. 2) Doctors must evaluate the morphological characteristics of blood vessels and plaques, such as the maximum or minimum diameter of the lumen, cross-sectional area, and plaque area. IVUS provides a reliable data support for doctors to diagnose patients objectively.3) IVUS can locate the region of interest to determine the position and shape of the anatomical structure for interventional surgery and the diagnosis and treatment targets for radiotherapy, chemotherapy, and surgery. However, given the different environments in which the intima and adventitia are located, traditional segmentation methods, which belong to serial extraction methods, need to design the segmentation algorithms for intima and adventitia separately. Moreover, extremely complex models affect the speed of segmentation. To address these problems, this paper proposes a segmentation method based on the extreme region detection of IVUS images.Method The problem of edge detection is broadened to the problem of extreme region detection, and the proposed method consists of three parts:extreme region detection, extreme region screening, and contour fitting. First, edge points are extracted from the IVUS image, and a global vector is created by using the edge points and threshold images by each gray level to obtain the gray thresholds. The obtained thresholds make the change of threshold images most stable.Next, the final threshold images are obtained on the basis of the filtered gray thresholds. The morphological closing operation is used to fill in the small holes of the threshold images, and the connected component labeling algorithm is used to mark the connected regions in the threshold images to obtain the final extreme regions. In addition, extreme regions contains regions with unstable states and large or small areas that cannot represent the lumen and media because the extracted extreme regions contain many sub-regions. Therefore, the area of the extreme regions must be screened for preliminary filtering.By using local binary mode feature, gray difference, and edge circumference, a filter vector based on region stability is designed to extract two extreme regions representing the lumen and media. Finally, the contours of the lumen and media regions are fitted by ellipse to complete the segmentation. Result Qualitative and quantitative analyses are used to evaluate the accuracy of the proposed method.The extreme region and final contours are initially qualitatively displayed on a standard published dataset containing 32 620 MHz IVUS B-mode images. The extracted final contours are qualitatively compared with the results drawn manually by clinical experts.The artifacts are also classified on the basis of their types. For images without artifacts and with different types of artifacts, the robust performance and generalization performance of the proposed algorithm are verified by calculating the DC coefficient, JI index, PAD index, and HD distance. On the basis of the DC coefficient, JI index, PAD index, and HD distance, the inner border index values are 0.94±0.02,0.90±0.04,0.05±0.05, and 0.28±0.14 mm, respectively; the outer border index values are 0.91±0.07,0.87±0.11, 0.11±0.11, and 0.41±0.31 mm, respectively.In addition, the values of DC coefficient, JI index, PAD index, and HD distance of the IVUS image segmentation algorithms in the relevant literature in the last few years are compared with those of the proposed method.The quantitative comparison with the relevant literature in the last few years shows the improved performance of the inner and outer borders extracted by the proposed method. In addition, the test results of the proposed method on the clinical dataset are very good.Conclusion The method proposed is suitable for the extraction of not only the inner border but also the outer border; it is a parallel extraction algorithm. Experiment results show that in addition to high extraction accuracy, the proposed method has strong robustness and outperforms several state-of-the-art segmentation approaches.