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A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images

  • Published:2012-08 Issue:10 Volume:39 Page:9661-9668
  • ISSN:0957-4174
  • Container-title:Expert Systems with Applications
  • language:en
  • Short-container-title:Expert Systems with Applications

Author:

Li Bing Nan,Chui Chee Kong,Chang Stephen,Ong Sim Heng

Funder

JSPS-Singapore Research Project

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference21 articles.

1. Moderate versus high concentration of contrast material for aortic and hepatic enhancement and tumor-to-liver contrast at multi-detector row CT;Awai;Radiology,2004

2. Level set segmentation with multiple regions;Brox;IEEE Transactions on Image Processing,2006

3. Diagnosis of liver disease by using CMAC neural network approach;Bucak;Expert Systems with Applications,2011

4. Active contours without edge;Chan;IEEE Transactions on Image Processing,2001

5. An improved level set for liver segmentation and perfusion analysis in MRIs;Chen;IEEE Transactions on Information Technology in Biomedicine,2009

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1. S2DA-Net: Spatial and spectral-learning double-branch aggregation network for liver tumor segmentation in CT images;Computers in Biology and Medicine;2024-05

2. Multi-task learning for segmentation and classification of breast tumors from ultrasound images;Computers in Biology and Medicine;2024-05

3. Application of YoloV5 for Brain Tumor Detection from MRI;2024 International Conference on Emerging Smart Computing and Informatics (ESCI);2024-03-05

4. Enhancing Liver Tumor Segmentation with Novel UNet++ Hybrid Models: ResNeXt, ResNet and InceptionV3;2023 Seventh International Conference on Image Information Processing (ICIIP);2023-11-22

5. Artificial intelligence-based detection and assessment of ascites on CT scans;Expert Systems with Applications;2023-08
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