scholarly journals Fast and accurate segmentation method of active shape model with Rayleigh mixture model clustering for prostate ultrasound images

2020 ◽  
Vol 184 ◽  
pp. 105097 ◽  
Author(s):  
Hui Bi ◽  
Yibo Jiang ◽  
Hui Tang ◽  
Guanyu Yang ◽  
Huazhong Shu ◽  
...  
Keyword(s):  
Mixture Model ◽  
Active Shape Model ◽  
Ultrasound Images ◽  
Segmentation Method ◽  
Shape Model ◽  
Active Shape ◽  
Mixture Model Clustering

scholarly journals Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Gurman Gill ◽  
Reinhard R. Beichel
Keyword(s):  
Image Data ◽  
Active Shape Model ◽  
Data Sets ◽  
Segmentation Method ◽  
Data Set ◽  
Shape Model ◽  
Lung Segmentation ◽  
4D Ct ◽  
Active Shape ◽  
Ct Data

Dynamic and longitudinal lung CT imaging produce 4D lung image data sets, enabling applications like radiation treatment planning or assessment of response to treatment of lung diseases. In this paper, we present a 4D lung segmentation method that mutually utilizes all individual CT volumes to derive segmentations for each CT data set. Our approach is based on a 3D robust active shape model and extends it to fully utilize 4D lung image data sets. This yields an initial segmentation for the 4D volume, which is then refined by using a 4D optimal surface finding algorithm. The approach was evaluated on a diverse set of 152 CT scans of normal and diseased lungs, consisting of total lung capacity and functional residual capacity scan pairs. In addition, a comparison to a 3D segmentation method and a registration based 4D lung segmentation approach was performed. The proposed 4D method obtained an average Dice coefficient of0.9773±0.0254, which was statistically significantly better (pvalue≪0.001) than the 3D method (0.9659±0.0517). Compared to the registration based 4D method, our method obtained better or similar performance, but was 58.6% faster. Also, the method can be easily expanded to process 4D CT data sets consisting of several volumes.

Influence of Active Shape Model Segmentation Method on Optical Reconstruction

2018 ◽  
Vol 38 (2) ◽  
pp. 0211001
Author(s):  
侯榆青 Hou Yuqing ◽  
胡昊文 Hu Haowen ◽  
赵凤军 Zhao Fengjun ◽  
何雪磊 He Xuelei ◽  
易黄建 Yi Huangjian ◽  
...  
Keyword(s):  
Active Shape Model ◽  
Segmentation Method ◽  
Shape Model ◽  
Model Segmentation ◽  
Active Shape ◽  
Optical Reconstruction

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

scholarly journals A medical imaging analysis system for trigger finger using an adaptive texture-based active shape model (ATASM) in ultrasound images

PLoS ONE ◽  
2017 ◽  
Vol 12 (10) ◽  
pp. e0187042 ◽  
Author(s):  
Bo-I Chuang ◽  
Li-Chieh Kuo ◽  
Tai-Hua Yang ◽  
Fong-Chin Su ◽  
I-Ming Jou ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Trigger Finger ◽  
Active Shape Model ◽  
Ultrasound Images ◽  
Imaging Analysis ◽  
Shape Model ◽  
Active Shape ◽  
Analysis System

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
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