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.

中文翻译：

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基于改进的超像素生成算法和分形理论的颈动脉内膜超声图像自动分割

客观的

颈动脉粥样硬化（CAS）是导致心血管疾病（如冠心病和脑血管疾病）的主要原因。在颈动脉超声图像中，可以在环形窄带中观察到颈动脉内膜中层结构，其内部轮廓对应于颈动脉内膜，而外部轮廓对应于颈动脉外膜。随着颈动脉粥样硬化的发展，颈动脉内膜中层将逐渐增厚。因此，医生可以观察颈动脉内膜中层，以获得患者颈动脉内部结构的病理变化。然而，由于伪影和噪声的存在，超声图像的质量下降，使得难以获得准确的颈内膜-中膜结构。

方法

在通过同态滤波和中值滤波对超声图像进行预处理之后，我们提出了一种改进的超像素生成算法，该算法利用灰度和基于亮度的信息的融合将图像分解为多个超像素，然后呈现出颈动脉内膜。同时，根据颈动脉的特征，通过归一化切割算法确定颈外膜的初始位置，然后采用分形理论对颈外膜进行分割。

结果

所提出的颈动脉内膜分割方法分别获得了DICE的真阳性率（TPR），假阳性率（FPR）和精确度得分的平均值，分别为97.797％，99.126％，0.540％，97.202％。除颈动脉外膜的分割方法外，获得的性能指标，例如平均DICE，TPR，准确性，F评分分别为95.00％，92.265％，97.689％，94.997％。

结论

与传统方法相比，该方法具有更好的性能。实验结果表明，该方法能够自动，准确地获得颈动脉内膜中层，从而有效地帮助医生诊断CAS。