Validation of active shape model techniques for intra-cochlear anatomy segmentation in CT images

2021 ◽  
Author(s):  
Rueben Banalagay ◽  
Robert F. Labadie ◽  
Jack H. Noble
Keyword(s):  
Ct Images ◽  
Active Shape Model ◽  
Shape Model ◽  
Active Shape

3D automatic liver segmentation using feature-constrained Mahalanobis distance in CT images

2016 ◽  
Vol 61 (4) ◽  
pp. 401-412 ◽  
Author(s):  
Saif Dawood Salman Al-Shaikhli ◽  
Michael Ying Yang ◽  
Bodo Rosenhahn
Keyword(s):  
Cost Function ◽  
Mahalanobis Distance ◽  
Texture Features ◽  
Disease Diagnosis ◽  
Ct Images ◽  
Image Features ◽  
Active Shape Model ◽  
Liver Segmentation ◽  
Shape Model ◽  
Active Shape

Abstract Automatic 3D liver segmentation is a fundamental step in the liver disease diagnosis and surgery planning. This paper presents a novel fully automatic algorithm for 3D liver segmentation in clinical 3D computed tomography (CT) images. Based on image features, we propose a new Mahalanobis distance cost function using an active shape model (ASM). We call our method MD-ASM. Unlike the standard active shape model (ST-ASM), the proposed method introduces a new feature-constrained Mahalanobis distance cost function to measure the distance between the generated shape during the iterative step and the mean shape model. The proposed Mahalanobis distance function is learned from a public database of liver segmentation challenge (MICCAI-SLiver07). As a refinement step, we propose the use of a 3D graph-cut segmentation. Foreground and background labels are automatically selected using texture features of the learned Mahalanobis distance. Quantitatively, the proposed method is evaluated using two clinical 3D CT scan databases (MICCAI-SLiver07 and MIDAS). The evaluation of the MICCAI-SLiver07 database is obtained by the challenge organizers using five different metric scores. The experimental results demonstrate the availability of the proposed method by achieving an accurate liver segmentation compared to the state-of-the-art methods.

Segmentation of parotid glands in head and neck CT images using a constrained active shape model with landmark uncertainty

2012 ◽  
Author(s):  
Antong Chen ◽  
Jack H. Noble ◽  
Kenneth J. Niermann ◽  
Matthew A. Deeley ◽  
Benoit M. Dawant
Keyword(s):  
Head And Neck ◽  
Ct Images ◽  
Active Shape Model ◽  
Parotid Glands ◽  
Shape Model ◽  
Active Shape

Segmentation of lymph node regions in head-and-neck CT images using a combination of registration and active shape model

2010 ◽  
Author(s):  
Antong Chen ◽  
Matthew A. Deeley ◽  
Kenneth J. Niermann ◽  
Luigi Moretti ◽  
Benoit M. Dawant
Keyword(s):  
Lymph Node ◽  
Head And Neck ◽  
Ct Images ◽  
Active Shape Model ◽  
Shape Model ◽  
Active Shape

Segmentation method for cardiac region in CT images based on active shape model

ICCAS 2010 ◽  
2010 ◽  
Author(s):  
Hiroki Takahashi ◽  
Masafumi Komatsu ◽  
Hyoungseop Kim ◽  
Joo Kooi Tan ◽  
Seiji Ishikawa ◽  
...  
Keyword(s):  
Ct Images ◽  
Active Shape Model ◽  
Segmentation Method ◽  
Shape Model ◽  
Cardiac Region ◽  
Active Shape

SU-GG-I-99: Registration and Active Shape Model-Based Segmentation of Lymph Node Regions in Head and Neck CT Images

2010 ◽  
Vol 37 (6Part4) ◽  
pp. 3124-3124
Author(s):  
A Chen ◽  
M Deeley ◽  
K Niermann ◽  
L Moretti ◽  
B Dawant
Keyword(s):  
Lymph Node ◽  
Head And Neck ◽  
Ct Images ◽  
Active Shape Model ◽  
Shape Model ◽  
Model Based ◽  
Active Shape

Human face shape classification method based on active shape model

2009 ◽  
Vol 29 (10) ◽  
pp. 2710-2712 ◽  
Author(s):  
Li-qiang DU ◽  
Peng JIA ◽  
Zong-tan ZHOU ◽  
De-wen HU
Keyword(s):  
Classification Method ◽  
Active Shape Model ◽  
Shape Classification ◽  
Human Face ◽  
Shape Model ◽  
Face Shape ◽  
Active Shape

Echocardiogram segmentation using active shape model and mean squared eigenvalue error

2021 ◽  
Vol 69 ◽  
pp. 102807
Author(s):  
Yasser Ali ◽  
Soosan Beheshti ◽  
Farrokh Janabi-Sharifi
Keyword(s):  
Active Shape Model ◽  
Shape Model ◽  
Active Shape
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