Automatic Vessel Detection without Shape Constraints from Ultrasound Images by Localized Fuzzy Energy-based Active Contour
Date Issued
2014
Date
2014
Author(s)
Lu, Kun-Han
Abstract
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%.
Subjects
超音波影像
血管偵測
無形狀限制
快速區域化前置分割處理
模糊能量基礎之形變模型
Type
thesis
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