Automated Measurement Method of Common Carotid Artery Intima-Media Thickness in Ultrasound Image Based on Markov Random Field Models

Current applications for measuring pediatric intima-media thickness

Intima-media thickness is a known subclinical radiologic marker of the early manifestations of atherosclerotic disease. It is the thickness of the vessel wall, most often the carotid artery. Intima-media thickness is measured on conventional US manually or automatically. Other measurement techniques include radiofrequency US. Because there is variation in its measurement, especially in children, several recommendations have been set to increase the measurement's validity and comparability among studies. Despite these recommendations, several pitfalls should be avoided, and quality control should be performed to avoid erroneous interpretation. This article summarizes current literature in relation to the clinical applications for intima-media thickness measurement in children with known risk factors such as obesity, liver steatosis, hypercholesterolemia, diabetes, hypertension, systemic inflammatory diseases, cancer survival, kidney and liver transplant, and sickle cell disease or beta thalassemia major. Most potential indications for intima-media thickness measurement remain in the research domain and should be interpreted combined with other markers. The objective of diagnosing an increased intima-media thickness is to start a multidisciplinary treatment approach to prevent disease progression and its sequelae in adulthood.

Fully Automatic Measurement of Intima-Media Thickness in Ultrasound Images of the Common Carotid Artery Based on Improved Otsu's Method and Adaptive Wind Driven Optimization

The intima media thickness (IMT) of the common carotid artery (CCA) can be used to predict the risk of atherosclerosis. Many image segmentation techniques have been used for IMT measurement. However, severe noise in the ultrasound image can lead to erroneous segmentation results. To improve the robustness to noise, a fully automatic method, based on an improved Otsu's method and an adaptive wind-driven optimization technique, is proposed for estimating the IMT (denoted as "improved Otsu-AWDO"). First, an advanced despeckling filter, i.e., " Nagare's filter" is used to address the speckle noise in the carotid ultrasound images. Next, an improved fuzzy contrast method (IFC) is used to enhance the region of the intima media complex (IMC) in the blurred filtered images. Then, a new method is used for automatic extraction of the region of interest (ROI). Finally, the lumen intima interface and media adventitia interface are segmented from the IMC using improved Otsu-AWDO. Then, 156 B-mode longitudinal carotid ultrasound images of six different datasets are used to evaluate the performance of the automatic measurements. The results indicate that the absolute error of proposed method is only 10.1 ± 9.6 (mean ± std in μm). Moreover, the proposed method has a correlation coefficient as high as 0.9922, and a bias as low as 0.0007. From comparison with previous methods, we can conclude that the proposed method has strong robustness and can provide accurate IMT estimations.

Automated localization and segmentation techniques for B-mode ultrasound images: A review

B-mode ultrasound imaging is used extensively in medicine. Hence, there is a need to have efficient segmentation tools to aid in computer-aided diagnosis, image-guided interventions, and therapy. This paper presents a comprehensive review on automated localization and segmentation techniques for B-mode ultrasound images. The paper first describes the general characteristics of B-mode ultrasound images. Then insight on the localization and segmentation of tissues is provided, both in the case in which the organ/tissue localization provides the final segmentation and in the case in which a two-step segmentation process is needed, due to the desired boundaries being too fine to locate from within the entire ultrasound frame. Subsequenly, examples of some main techniques found in literature are shown, including but not limited to shape priors, superpixel and classification, local pixel statistics, active contours, edge-tracking, dynamic programming, and data mining. Ten selected applications (abdomen/kidney, breast, cardiology, thyroid, liver, vascular, musculoskeletal, obstetrics, gynecology, prostate) are then investigated in depth, and the performances of a few specific applications are compared. In conclusion, future perspectives for B-mode based segmentation, such as the integration of RF information, the employment of higher frequency probes when possible, the focus on completely automatic algorithms, and the increase in available data are discussed.