scholarly journals Automatic Segmentation of Renal Parenchyma using Graph-cuts with Shape Constraint based on Multi-probabilistic Atlas in Abdominal CT Images

2016 ◽  
Vol 22 (4) ◽  
pp. 11-19
Author(s):  
이재선 ◽  
홍헬렌 ◽  
나군호
Keyword(s):  
Automatic Segmentation ◽  
Graph Cuts ◽  
Ct Images ◽  
Renal Parenchyma ◽  
Abdominal Ct ◽  
Shape Constraint ◽  
Probabilistic Atlas

Automatic Segmentation of Renal Parenchyma using Shape and Intensity Information based on Multi-atlas in Abdominal CT Images

2018 ◽  
Vol 45 (9) ◽  
pp. 937-942
Author(s):  
Hyeonjin Kim ◽  
Helen Hong ◽  
Kidon Chang ◽  
Koon Ho Rha
Keyword(s):  
Automatic Segmentation ◽  
Ct Images ◽  
Renal Parenchyma ◽  
Abdominal Ct ◽  
Intensity Information

Multi-organ segmentation from 3D abdominal CT images using patient-specific weighted-probabilistic atlas

2013 ◽  
Author(s):  
Chengwen Chu ◽  
Masahiro Oda ◽  
Takayuki Kitasaka ◽  
Kazunari Misawa ◽  
Michitaka Fujiwara ◽  
...  
Keyword(s):  
Ct Images ◽  
Patient Specific ◽  
Abdominal Ct ◽  
Probabilistic Atlas

scholarly journals A Multiatlas Segmentation Using Graph Cuts with Applications to Liver Segmentation in CT Scans

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Carlos Platero ◽  
M. Carmen Tobar
Keyword(s):  
Graph Cuts ◽  
Computational Effort ◽  
Segmentation Method ◽  
Abdominal Ct ◽  
Discrete Energy ◽  
Affine Registration ◽  
Low Level ◽  
Probabilistic Atlas ◽  
Segmentation Approach ◽  
Better Than

An atlas-based segmentation approach is presented that combines low-level operations, an affine probabilistic atlas, and a multiatlas-based segmentation. The proposed combination provides highly accurate segmentation due to registrations and atlas selections based on the regions of interest (ROIs) and coarse segmentations. Our approach shares the following common elements between the probabilistic atlas and multiatlas segmentation: (a) the spatial normalisation and (b) the segmentation method, which is based on minimising a discrete energy function using graph cuts. The method is evaluated for the segmentation of the liver in computed tomography (CT) images. Low-level operations define a ROI around the liver from an abdominal CT. We generate a probabilistic atlas using an affine registration based on geometry moments from manually labelled data. Next, a coarse segmentation of the liver is obtained from the probabilistic atlas with low computational effort. Then, a multiatlas segmentation approach improves the accuracy of the segmentation. Both the atlas selections and the nonrigid registrations of the multiatlas approach use a binary mask defined by coarse segmentation. We experimentally demonstrate that this approach performs better than atlas selections and nonrigid registrations in the entire ROI. The segmentation results are comparable to those obtained by human experts and to other recently published results.

Texture-learning-based system for three-dimensional segmentation of renal parenchyma in abdominal CT images

2009 ◽  
Author(s):  
Cong-Qi Peng ◽  
Yuan-Hsiang Chang ◽  
Li-Jen Wang ◽  
Yon-Choeng Wong ◽  
Yang-Jen Chiang ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Ct Images ◽  
Renal Parenchyma ◽  
Abdominal Ct ◽  
Three Dimensional Segmentation

scholarly journals Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Weiwei Wu ◽  
Zhuhuang Zhou ◽  
Shuicai Wu ◽  
Yanhua Zhang
Keyword(s):  
Liver Volume ◽  
Maximum Intensity Projection ◽  
Liver Slice ◽  
Graph Cuts ◽  
Ct Images ◽  
Patient Specific ◽  
Computer Assisted ◽  
Morphological Operations ◽  
Liver Segmentation ◽  
Abdominal Ct

Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despite many years of research, automatic liver segmentation remains a challenging task. In this paper, a novel method was proposed for automatic delineation of liver on CT volume images using supervoxel-based graph cuts. To extract the liver volume of interest (VOI), the region of abdomen was firstly determined based on maximum intensity projection (MIP) and thresholding methods. Then, the patient-specific liver VOI was extracted from the region of abdomen by using a histogram-based adaptive thresholding method and morphological operations. The supervoxels of the liver VOI were generated using the simple linear iterative clustering (SLIC) method. The foreground/background seeds for graph cuts were generated on the largest liver slice, and the graph cuts algorithm was applied to the VOI supervoxels. Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm. Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases.

Sign in / Sign up

Export Citation Format

Share Document