scholarly journals Variable Background Active Contour Model for Computer-Aided Delineation of Nodules in Thyroid Ultrasound Images

2007 ◽  
Vol 11 (5) ◽  
pp. 537-543 ◽  
Author(s):  
Dimitris E. Maroulis ◽  
Michalis A. Savelonas ◽  
Dimitris K. Iakovidis ◽  
Stavros A. Karkanis ◽  
Nikos Dimitropoulos
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Ultrasound Images ◽  
Thyroid Ultrasound ◽  
Contour Model ◽  
Computer Aided

Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model

2018 ◽  
Vol 46 (1) ◽  
pp. 215-228 ◽  
Author(s):  
Yuzhou Hu ◽  
Yi Guo ◽  
Yuanyuan Wang ◽  
Jinhua Yu ◽  
Jiawei Li ◽  
...  
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Breast Ultrasound ◽  
Tumor Segmentation ◽  
Ultrasound Images ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Contour Model

scholarly journals A variable background active contour model for automatic detection of thyroid nodules in ultrasound images

2005 ◽  
Author(s):  
M. Savelonas ◽  
D. Maroulis ◽  
D. Iakovidis ◽  
S. Karkanis ◽  
N. Dimitropoulos
Keyword(s):  
Active Contour ◽  
Thyroid Nodules ◽  
Active Contour Model ◽  
Automatic Detection ◽  
Ultrasound Images ◽  
Contour Model

A region and gradient based active contour model and its application in boundary tracking on anal canal ultrasound images

2007 ◽  
Vol 40 (12) ◽  
pp. 3522-3539 ◽  
Author(s):  
Di Xiao ◽  
Wan Sing Ng ◽  
Charles B. Tsang ◽  
Udantha Ranjith Abeyratne
Keyword(s):  
Anal Canal ◽  
Active Contour ◽  
Active Contour Model ◽  
Ultrasound Images ◽  
Boundary Tracking ◽  
Contour Model ◽  
Gradient Based

scholarly journals Active Contour Model for Ultrasound Images with Rayleigh Distribution

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Guodong Wang ◽  
Qian Dong ◽  
Zhenkuan Pan ◽  
Ximei Zhao ◽  
Jinbao Yang ◽  
...  
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Speckle Noise ◽  
Rayleigh Distribution ◽  
Ultrasound Images ◽  
Fast Method ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Multiplicative Noises ◽  
Contour Model

Ultrasound images are often corrupted by multiplicative noises with Rayleigh distribution. The noises are strong and often called speckle noise, so segmentation is a hard work with this kind of noises. In this paper, we incorporate multiplicative noise removing model into active contour model for ultrasound images segmentation. To model gray level behavior of ultrasound images, the classic Rayleigh probability distribution is considered. Our model can segment the noisy ultrasound images very well. Finally, a fast method called Split-Bregman method is used for the easy implementation of segmentation. Experiments on a variety of synthetic and real ultrasound images validate the performance of our method.

Combining a Fully Convolutional Network and an Active Contour Model for Automatic 2D Breast Tumor Segmentation from Ultrasound Images

2019 ◽  
Vol 9 (7) ◽  
pp. 1510-1515 ◽  
Author(s):  
Zhou Fang ◽  
Mengyun Qiao ◽  
Yi Guo ◽  
Yuanyuan Wang ◽  
Jiawei Li ◽  
...  
Keyword(s):  
Breast Tumor ◽  
Active Contour ◽  
Active Contour Model ◽  
Tumor Segmentation ◽  
Ultrasound Images ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Contour Model

A novel active contour model for fully automated segmentation of intravascular ultrasound images: In vivo validation in human coronary arteries

2007 ◽  
Vol 37 (9) ◽  
pp. 1292-1302 ◽  
Author(s):  
George D. Giannoglou ◽  
Yiannis S. Chatzizisis ◽  
Vassilis Koutkias ◽  
Ioannis Kompatsiaris ◽  
Maria Papadogiorgaki ◽  
...  
Keyword(s):  
Intravascular Ultrasound ◽  
Coronary Arteries ◽  
Active Contour ◽  
Active Contour Model ◽  
Ultrasound Images ◽  
Automated Segmentation ◽  
Contour Model ◽  
In Vivo Validation
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