Medical ultrasound image segmentation and biological tissue characterization

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Ultrasound imaging has traditionally been used to diagnose medical conditions and is also developed for therapy purpose where the precise identification of the targeted organ or structure is a prerequisite. Ultrasound image segmentation consists in delineating boundaries of physical structures with the objective of localizing them, measuring their dimensions, characterizing the underlying tissue properties or to support inverse problem formulation for more advance characterizations. Traditional and more recent image segmentation methods applied to ultrasound images rely typically on pixel-based, region-based, intensity gradient-based, texture-based, local-phase-based and statistical speckle property-based features integrated into active contour, deformable template, watershed, neural network, Bayesian, multi-agent, level set and fast-marching methods. The major challenges in segmenting ultrasound images versus other radiology imaging modalities such as MRI, CT and angiography are the noisy nature of those images, missing information due to shadowing and signal attenuation, low contrast, and movements associated with dynamic image acquisitions and ultrasound image-guided interventions. Consequently, most methods require anatomical, geometric, intensity, temporal and image physics priors. A promising innovative avenue is to incorporate additional image physics priors to address even more challenging problems, such as the sub-segmentation of structures with common cellular properties within pre-segmented organs. In addition to speckle statistics, tissue characterization features based on the spectral content of radio-frequency echoes can be integrated into the nonlinear optimization function of segmentation methods to enhance performance. This will be the objective of this research program that has been synergically designed to support my ultrasound research activities on carotid and coronary atherosclerotic plaque characterization, breast cancer diagnosis and imaging of blood clots. The proposed program will also support artivities of my laboratory on diseased tendon characterization, endocardiac border detection and photo-acoustic imaging.