Literature DB >> 24034738

Object information based interactive segmentation for fatty tissue extraction.

Zhi-Guo Zhou1, Fang Liu, Li-Cheng Jiao, Ling-Ling Li, Xiao-Dong Wang, Shui-Ping Gou, Shuang Wang.   

Abstract

Lymph nodes are very important factors for diagnosing gastric cancer in clinical use, and are usually distributed within the fatty tissue around the stomach. When extracting fatty tissues whose structures and textures are complicated, automatic extraction is still a challenging task, while manual extraction is time-consuming. Consequently, semi-automatic extraction, which allows introducing interactive operations, appears to be more realistic. Currently, most interactive methods need to indicate the position and main features in both the object and background. However, it is easier for radiologists to only mark object information. Due to this issue, a new Object Information based Interactive Segmentation (OIIS) method is proposed in this paper. Different from the most existing methods, OIIS just needs to input the object information, while the background information is not required. Experimental results and comparative studies show that OIIS is effective for fatty tissue extraction.
Copyright © 2013 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Fatty tissue; Gastric cancer; Interactive image segmentation; Mean shift; Object information

Mesh:

Year:  2013        PMID: 24034738     DOI: 10.1016/j.compbiomed.2013.07.023

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  5 in total

1.  Multi-Objective-Based Radiomic Feature Selection for Lesion Malignancy Classification.

Authors:  Zhiguo Zhou; Shulong Li; Genggeng Qin; Michael Folkert; Steve Jiang; Jing Wang
Journal:  IEEE J Biomed Health Inform       Date:  2019-02-28       Impact factor: 5.772

2.  A pilot study using kernelled support tensor machine for distant failure prediction in lung SBRT.

Authors:  Shulong Li; Ning Yang; Bin Li; Zhiguo Zhou; Hongxia Hao; Michael R Folkert; Puneeth Iyengar; Kenneth Westover; Hak Choy; Robert Timmerman; Steve Jiang; Jing Wang
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3.  Multi-objective radiomics model for predicting distant failure in lung SBRT.

Authors:  Zhiguo Zhou; Michael Folkert; Puneeth Iyengar; Kenneth Westover; Yuanyuan Zhang; Hak Choy; Robert Timmerman; Steve Jiang; Jing Wang
Journal:  Phys Med Biol       Date:  2017-05-08       Impact factor: 3.609

4.  Accurate segmenting of cervical tumors in PET imaging based on similarity between adjacent slices.

Authors:  Liyuan Chen; Chenyang Shen; Zhiguo Zhou; Genevieve Maquilan; Kimberly Thomas; Michael R Folkert; Kevin Albuquerque; Jing Wang
Journal:  Comput Biol Med       Date:  2018-04-16       Impact factor: 4.589

5.  Shell feature: a new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervix cancer.

Authors:  Hongxia Hao; Zhiguo Zhou; Shulong Li; Genevieve Maquilan; Michael R Folkert; Puneeth Iyengar; Kenneth D Westover; Kevin Albuquerque; Fang Liu; Hak Choy; Robert Timmerman; Lin Yang; Jing Wang
Journal:  Phys Med Biol       Date:  2018-05-02       Impact factor: 3.609

  5 in total

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