Please use this identifier to cite or link to this item:
|Title: ||Automatic Liver Segmentation from CT Images Using Latent Semantic Indexing|
|Authors: ||Chun-Yao Hsieh|
NTOU:Department of Computer Science and Engineering
Training, Image segmentation
|Issue Date: ||2018-01-31T03:06:39Z
|Publisher: ||Multimedia Signal Processing (MMSP), 2015 IEEE 17th International Workshop on|
In this paper, we present an indexing structure of data-driven cuboid patterns to speed up the process of liver detection and segmentation from computed tomography (CT) scans using the cube-based generalized Hough transform (CGHT). Most existing approaches to automatic liver segmentation from CT scans use a statistical shape model (SSM) integrated with a searching algorithm to recover the deformation. However, establishing the correspondences among landmark points of training shapes for the construction of the average shape of SSM remains as a challenge due to the high variation of liver shapes in CT scans. The proposed method is a fully automatic segmentation method that combines four steps. Firstly, a test CT volume is partitioned into multiple non-overlapped sub-volumes with each of them consisting of variable numbers of consecutive slices. Secondly, we locate the cube-based liver shapes in all sub-volumes via Hough voting and dynamic programming. Thirdly, we construct the basic 3D model through a level-set framework for liver shape segmentation. Finally, to introduce neighbors statistical analysis into the above model, we deform the 3D liver shape to overcome disturbances caused by noise and inhomogeneity. The MICCAI 2007 liver segmentation challenge datasets are used to verify the effectiveness of the proposed method. Experimental results demonstrate the good performance of the proposed method in terms of segmentation accuracy and execution speed.
|Appears in Collections:||[資訊工程學系] 演講及研討會|
Files in This Item:
All items in NTOUR are protected by copyright, with all rights reserved.