Automatic segmentation for ultrasound image of carotid intimal-media based on improved superpixel generation algorithm and fractal theory

• Published in: Computer methods and programs in biomedicine Vol. 205; p. 106084
• Main Authors: Zhuang, Shuxin, Li, Fenlan, Raj, Alex Noel Joseph, Ding, Wanli, Zhou, Wang, Zhuang, Zhemin
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.06.2021
• Subjects: Carotid atherosclerosis diagnosis; Carotid ultrasound image; Fractal theory; Normalized cut; Superpixel; Superpixel; Fractal theory; Normalized cut; Carotid atherosclerosis diagnosis; Carotid ultrasound image
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2021.106084

•A new self-adaptive automatic method is implemented to segment carotid intima-media.•Homomorphic filtering and median filtering are introduced as a preprocessing to give prominence to edges of the ultrasound image•SLIC algorithm incorporates both grayscale- and luminosity- based information obtained higher boundary recall ratio.•Normalized cut algorithm is employed to decompose the ultrasound image into a few superpixels and locate the carotid extima.•The proposed method performed well which is of great significance to observe the lesions of carotid artery. 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. 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. 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. 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.