| Ultrasound is a popular clinical imaging modality because of its low cost, safety, and portability. However, its fuzzy image quality leads to difficulty in diagnoses. Therefore, there is an increasing interest to use quantitative methods to help interpret ultrasound images by analyzing tissue mechanical properties, predominantly in musculoskeletal system and cardiovascular system. However, to analyze mechanical properties of ultrasound images, it is necessary to segment out the pathological (or healing) region in the ultrasound image and compare this with the intact region for mechanical property analysis. Therefore, this thesis developed an algorithm called Projected Empirical Segmentation (PES) that segments and tracks the region of interest (e.g. pathological region) in an ultrasound video. In addition, this thesis also developed another algorithm called Spatial and Frequency-Based Super-Resolution that improves the resolution and quality of ultrasound images. This could be significantly helpful in both scientific applications and in clinical diagnoses. Tests of both algorithms have been made on Achilles tendons, blood vessels, and breast tumors phantoms, and they showed promising results and the potential to significantly improve the accuracy and reliability of quantitative methods. |
