SEGMENTATION OF VESSEL GEOMETRIES FROM MEDICAL IMAGES USING GPF DEFORMABLE MODEL
Si Yong Yeo, Xianghua Xie, Perumal Nithiarasu, Igor Sazanov · 2012
Abstract: We present a method for the reconstruction of vascular geometries from medical images. Image denoising is performed using vessel enhancing diffusion, which can smooth out image noise and enhance vessel structures. The Canny edge detection technique which produces object edges with single pixel width is used for accurate detection of the lumen boundaries. The image gradients are then used to compute the geometric potential field which gives a global representation of the geometric configuration. The deformable model uses a regional constraint to suppress calcified regions for accurate segmentation of the vessel geometries. The proposed framework show high accuracy when applied to the segmentation of the carotid arteries from CT images. 1