Border identification of the atherosclerotic carotid plaque, the common carotid artery (CCA), degree of stenosis, as well as the characteristics of the arterial wall (plaque size, composition and elasticity), may add additional clinical information for the assessment of future cardiovascular events. We propose and evaluate in this chapter an integrated system for the segmentation of atherosclerotic carotid plaque in ultrasound videos of the CCA. The system is based on video frame normalization, speckle reduction filtering, M-mode-state-based identification, parametric active contours and snake's segmentation. The cardiac cycle in each video is first identified and the video M-mode is generated, thus identifying systolic and diastolic states. The video is segmented for a time period of at least one full cardiac cycle by initializing the algorithm in the first video frame. Human manual assistance may be provided if needed. The atherosclerotic plaque borders are tracked and segmented in the subsequent frames. We also propose an initialization method for positioning the snake as close as possible to the plaque borders, based on morphology operators, where initial contours are estimated every 20 video frames. The performance of the algorithm is evaluated on 43 real CCA digitized videos from B-mode longitudinal ultrasound segments and is compared to the manual segmentations of an expert, available every 20 frames in a time span of 3-5 s, covering in general two cardiac cycles. The segmentation results were very promising, according to the expert objective evaluation, with a true-negative fraction (TNF) specificity of 83.7% + 7.6%, a true-positive fraction (TPF) sensitivity of 85.42% + 8.1%, between the ground truth and the proposed segmentation method, a kappa index (KI) of 84.6% and an overlap index (OI) of 74.7%. We also computed the cardiac state identification for the CCA. It is shown that the integrated system presented in this chapter can be used for the video segmentation of the CCA plaque in ultrasound videos.

Chapter Contents:

• 20.1 Introduction
• 20.2 Methodology and materials used
• 20.2.1 Acquisition of ultrasound videos and manual delineation of atherosclerotic plaque
• 20.2.2 Video normalization and speckle reduction filtering of ultrasound videos
• 20.2.3 Plaque contour initialization and snakes segmentation
• 20.2.4 M-mode image generation, boundary extraction, state identification and manual delineation
• 20.2.5 Evaluation of the segmentation method and state diagram
• 20.3 Results
• 20.4 Discussion
• 20.4.1 Limitations of the video segmentation method
• 20.5 Concluding remarks
• References

Inspec keywords: video signal processing; image segmentation; cardiovascular system; blood vessels; biomedical ultrasonics; diseases; image filtering; medical image processing

Other keywords: morphology operators; speckle reduction filtering; overlap index; common carotid artery; true-negative fraction specificity; stenosis; parametric active contours; diastolic states; systolic states; ultrasound carotid plaque video segmentation; video M-mode; snake segmentation; M-mode-state-based identification; atherosclerotic carotid plaque; cardiac cycle; video frame; cardiovascular events; kappa index; video frame normalization; true-positive fraction sensitivity; arterial wall; B-mode longitudinal ultrasound segments

Subjects: Optical, image and video signal processing; Biology and medical computing; Sonic and ultrasonic radiation (biomedical imaging/measurement); Video signal processing; Sonic and ultrasonic applications; Sonic and ultrasonic radiation (medical uses); Computer vision and image processing techniques; Patient diagnostic methods and instrumentation