A spatiotemporal neural network on dynamic Gd-enhanced MR images for diagnosing recurrent nasal papilloma

Chuan Yu Chang, Pau Choo Chung, E. Liang Chen, Wen Chen Huang, Ping Hong Lai

Research output: Contribution to journalArticle

Abstract

The purpose of this paper is to develop an automatic diagnosis system for distinguishing between tumor and fibrosis in the nasal region. The proposed system is composed of a new model, Relative Intensity Change (RIC), for point matching among the consecutive MR image sequence, and a Spatiotemporal Neural Network (STNN) for distinguishing between the tumor and fibrosis. Then, a knowledge-based refinement process is applied for extracting the tumor/fibrosis. A color-code representation of the different abnormal regions are displayed. The experimental results show that the proposed method is able to detect the abnormal tissues precisely.

Original languageEnglish
Pages (from-to)3056-3059
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume4
DOIs
Publication statusPublished - 2000 Jan 1

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Papilloma
Nose
Tumors
Fibrosis
Neural networks
Color codes
Neoplasms
Color
Tissue

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

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A spatiotemporal neural network on dynamic Gd-enhanced MR images for diagnosing recurrent nasal papilloma. / Chang, Chuan Yu; Chung, Pau Choo; Chen, E. Liang; Huang, Wen Chen; Lai, Ping Hong.

In: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, Vol. 4, 01.01.2000, p. 3056-3059.

Research output: Contribution to journalArticle

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