Please use this identifier to cite or link to this item:
Title: Compression of 4D medical image and spatial segmentation using deformable models
Keywords: Medical image compression, lossy-to-lossless, medical image segmentation, deformable models, shape prior, capillary action.
Issue Date: 22-May-2006
Source: YAN PINGKUN (2006-05-22). Compression of 4D medical image and spatial segmentation using deformable models. ScholarBank@NUS Repository.
Abstract: Medical imaging has become an essential tool to assist physicians in clinical routines. The problem of huge data volume for storage or communications becomes more acute for 4D medical images (i.e., 3D volumetric images over time). A new 3D motion compensated lossy-to-lossless 4D medical image compression scheme using the integer wavelet transform is proposed. Another major issue in medical imaging is automatic or semi-automatic extraction of critical diagnostic information. In our proposed minimal path deformable model based approach, the initialization for the segmentation process is significantly simplified into a single starting point and precise and robust segmentation is achieved. Extracting complex vasculature from 3D magnetic resonance angiography is also studied. Inspired by the capillary action, a capillary active contour is modeled to extract blood vessels from MRA images. Experimental results show that the capillary active contour can achieve more precise segmentation when compared with other state-of-the-art algorithms.
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Yan_Pingkun_PhD_thesis.pdf3.39 MBAdobe PDF



Page view(s)

checked on Jan 21, 2018


checked on Jan 21, 2018

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.