基于改进超像素生成算法和分形理论的颈动脉内膜中层超声图像自动分割
Automatic segmentation for ultrasound image of carotid intimal-media based on improved superpixel generation algorithm and fractal theory.
作者信息
Zhuang Shuxin, Li Fenlan, Raj Alex Noel Joseph, Ding Wanli, Zhou Wang, Zhuang Zhemin
机构信息
Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Shantou University, Shantou, Guangdong, China; Department of Electronic Engineering, Shantou University, Shantou, Guangdong, China.
Department of Electronic Engineering, Shantou University, Shantou, Guangdong, China.
出版信息
Comput Methods Programs Biomed. 2021 Jun;205:106084. doi: 10.1016/j.cmpb.2021.106084. Epub 2021 Apr 6.
OBJECTIVE
Carotid atherosclerosis (CAS) is the main reason leading to cardiovascular conditions such as coronary heart disease and cerebrovascular diseases. In the carotid ultrasound images, the carotid intima-media structure can be observed in an annular narrow strip, which its inner contour corresponds to the carotid intima, and the outer contour corresponds to the carotid extima. With the development of carotid atherosclerosis, the carotid intima-media will gradually thicken. Therefore, doctors can observe the carotid intima-media so as to obtain the pathological changes of the internal structure of the patient's carotid arteries. However, due to the presence of artifacts and noises the quality of the ultrasound images are degraded, making it difficult to obtain accurate carotid intima-media structures. This article presents a novel self-adaptive method to enable obtaining the carotid intima-media through carotid intima/extima segmentation.
METHOD
After preprocessing the ultrasound images by homomorphic filtering and median filtering, we propose an improved superpixel generation algorithm that employs the fusion of gray-level and luminosity-based information to decompose the image into numerous superpixels and later presents the carotid intima. Meanwhile, based on the features of the carotid artery, the initial position of the carotid extima is located by the normalized cut algorithm and later the fractal theory is employed to segment the carotid extima.
RESULTS
The proposed method for segmenting carotid intima obtained mean values of the DICE true positive ratio (TPR), false positive ratio (FPR), precision scores of 97.797%, 99.126%, 0.540%, 97.202%, respectively. Further from the segmentation method of the carotid extima the performance measures such as mean DICE, TPR, accuracy, F-score obtained are 95.00%, 92.265%, 97.689%, 94.997%, respectively.
CONCLUSION
Comparing with traditional methods, the proposed method performed better. The experimental results indicated that the proposed method obtained the carotid intima-media both automatically and accurately thus effectively assist doctors in the diagnosis of CAS.
目的
颈动脉粥样硬化（CAS）是导致冠心病和脑血管疾病等心血管疾病的主要原因。在颈动脉超声图像中，颈动脉内膜中层结构呈现为环形窄带，其内部轮廓对应颈动脉内膜，外部轮廓对应颈动脉外膜。随着颈动脉粥样硬化的发展，颈动脉内膜中层会逐渐增厚。因此，医生可以通过观察颈动脉内膜中层来获取患者颈动脉内部结构的病理变化。然而，由于伪像和噪声的存在，超声图像质量下降，难以获得准确的颈动脉内膜中层结构。本文提出一种新颖的自适应方法，通过颈动脉内膜/外膜分割来获取颈动脉内膜中层。
方法
在对超声图像进行同态滤波和中值滤波预处理后，我们提出一种改进的超像素生成算法，该算法融合基于灰度级和亮度的信息将图像分解为多个超像素，进而呈现颈动脉内膜。同时，基于颈动脉的特征，通过归一化切割算法确定颈动脉外膜的初始位置，随后采用分形理论对颈动脉外膜进行分割。
结果
所提出的颈动脉内膜分割方法的DICE真阳性率（TPR）、假阳性率（FPR）、精确率得分的平均值分别为97.797%、99.126%、0.540%、97.202%。进一步来看，颈动脉外膜分割方法获得的平均DICE、TPR、准确率、F值等性能指标分别为95.00%、92.265%、97.689%、94.997%。
结论
与传统方法相比，所提出的方法表现更优。实验结果表明，该方法能自动且准确地获取颈动脉内膜中层，从而有效辅助医生诊断CAS。
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