Literature DB >> 14654153

Segmentation of breast tumor in three-dimensional ultrasound images using three-dimensional discrete active contour model.

Ruey Feng Chang1, Wen Jie Wu, Woo Kyung Moon, Wei Ming Chen, Wei Lee, Dar Ren Chen.   

Abstract

In this paper, we apply the three-dimensional (3-D) active contour model to a 3-D ultrasonic data file for segmenting of the breast tumor. The 3-D ultrasonic file is composed of a series of two-dimensional (2-D) images. Most of traditional techniques of 2-D image segmentation will not use the information between adjacent images. To suit the property of the 3-D data, we introduce the concept of the 3-D stick, the 3-D morphologic process and the 3-D active contour model. The 3-D stick can get over the problem that the ultrasonic image is full of speckle noise and highlight the edge information in images. The 3-D morphologic process helps to determine the contour of the tumor and the resulting contour can be regarded as the initial contour of the active contour model. Finally, the 3-D active contour model will make the initial contour approach to the real contour of the tumor. However, there is emphasis on these 3-D techniques that they do not consist of a series of 2-D techniques. When they work, they will consider the horizontal, vertical and depth directions at the same time. The use of these 3-D techniques not only segments the 3-D shape but also obtains the volume of the tumor. The volume of the tumor calculated by the proposed method will be compared with the volume calculated by the VOCAL software with the physician's manually drawn shape and it shows that the performance of our method is satisfactory.

Entities:  

Mesh:

Year:  2003        PMID: 14654153     DOI: 10.1016/s0301-5629(03)00992-x

Source DB:  PubMed          Journal:  Ultrasound Med Biol        ISSN: 0301-5629            Impact factor:   2.998


  10 in total

1.  An effective approach of lesion segmentation within the breast ultrasound image based on the cellular automata principle.

Authors:  Yan Liu; H D Cheng; Jianhua Huang; Yingtao Zhang; Xianglong Tang
Journal:  J Digit Imaging       Date:  2012-10       Impact factor: 4.056

2.  Segmentation of elastographic images using a coarse-to-fine active contour model.

Authors:  Wu Liu; James A Zagzebski; Tomy Varghese; Charles R Dyer; Udomchai Techavipoo; Timothy J Hall
Journal:  Ultrasound Med Biol       Date:  2006-03       Impact factor: 2.998

3.  Automated volume analysis of head and neck lesions on CT scans using 3D level set segmentation.

Authors:  Ethan Street; Lubomir Hadjiiski; Berkman Sahiner; Sachin Gujar; Mohannad Ibrahim; Suresh K Mukherji; Heang-Ping Chan
Journal:  Med Phys       Date:  2007-11       Impact factor: 4.071

4.  Segmentation of ultrasonic breast tumors based on homogeneous patch.

Authors:  Liang Gao; Wei Yang; Zhiwu Liao; Xiaoyun Liu; Qianjin Feng; Wufan Chen
Journal:  Med Phys       Date:  2012-06       Impact factor: 4.071

5.  Automated 3D ultrasound image segmentation to aid breast cancer image interpretation.

Authors:  Peng Gu; Won-Mean Lee; Marilyn A Roubidoux; Jie Yuan; Xueding Wang; Paul L Carson
Journal:  Ultrasonics       Date:  2015-10-31       Impact factor: 2.890

6.  A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images.

Authors:  Yaozhong Luo; Longzhong Liu; Qinghua Huang; Xuelong Li
Journal:  Biomed Res Int       Date:  2017-04-27       Impact factor: 3.411

Review 7.  BUSIS: A Benchmark for Breast Ultrasound Image Segmentation.

Authors:  Yingtao Zhang; Min Xian; Heng-Da Cheng; Bryar Shareef; Jianrui Ding; Fei Xu; Kuan Huang; Boyu Zhang; Chunping Ning; Ying Wang
Journal:  Healthcare (Basel)       Date:  2022-04-14

8.  Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.

Authors:  Anca Ciurte; Xavier Bresson; Olivier Cuisenaire; Nawal Houhou; Sergiu Nedevschi; Jean-Philippe Thiran; Meritxell Bach Cuadra
Journal:  PLoS One       Date:  2014-07-10       Impact factor: 3.240

9.  Automated and real-time segmentation of suspicious breast masses using convolutional neural network.

Authors:  Viksit Kumar; Jeremy M Webb; Adriana Gregory; Max Denis; Duane D Meixner; Mahdi Bayat; Dana H Whaley; Mostafa Fatemi; Azra Alizad
Journal:  PLoS One       Date:  2018-05-16       Impact factor: 3.240

10.  BGM-Net: Boundary-Guided Multiscale Network for Breast Lesion Segmentation in Ultrasound.

Authors:  Yunzhu Wu; Ruoxin Zhang; Lei Zhu; Weiming Wang; Shengwen Wang; Haoran Xie; Gary Cheng; Fu Lee Wang; Xingxiang He; Hai Zhang
Journal:  Front Mol Biosci       Date:  2021-07-19
  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.