Automatic Vessel Detection without Shape Constraints from Ultrasound Images by Localized Fuzzy Energy-based Active Contour

• Main Authors: Kun-Han Lu, 呂昆翰
• Other Authors: Yung-Yaw Chen
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/54161984620847511739

碩士 === 國立臺灣大學 === 電機工程學研究所 === 102 === Vessel detection from ultrasound images could be widely applied to computer aided diagnosis, image-guided online treatments and image registration between different imaging modalities. However, since the image quality of ultrasound images is often degraded by speckle noise, intensity inhomogeneity and low contrast, previous vessel detection approaches could not achieve the process of vessel detection automatically along with high accuracy and without certain shape constraints. Besides, they are not able to detect vessels with ambiguous boundary. In this thesis, a novel approach for detecting vessels automatically and robustly is proposed. Hence we propose a fast localized preliminary segmentation approach combined with fuzzy energy-based active contour so that it can deal with intensity inhomogeneity and it is able to segment objects with ambiguous boundary in one iteration. Apart from previous approaches, our approach does not require manual intervention and does not need a prior knowledge on the shape of vessels. The result of this study shows that we could process one frame with average processing time of 0.197 seconds and the overall accuracy of vessel detection is 89.4%.