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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/45160

Title: Automatic Liver Segmentation from CT Images Using Latent Semantic Indexing
Authors: Chun-Yao Hsieh
Shyi-Chyi Cheng
Chin-Chun Chang
Chin-Lang Lin
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Liver
Shape
Computed tomography
Three-dimensional displays
Training, Image segmentation
Solid modeling
Date: 2015-10
Issue Date: 2018-01-31T03:06:39Z
Publisher: Multimedia Signal Processing (MMSP), 2015 IEEE 17th International Workshop on
Abstract: Abstract:
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.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/45160
Appears in Collections:[資訊工程學系] 演講及研討會

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