Markerless fluoroscopic gating for lung cancer radiotherapy using generalized linear discriminant analysis

Ruijiang Li, John H. Lewis, Steve B. Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Respiratory gated radiotherapy for lung cancer allows for more precise delivery of prescribed radiation dose to the tumor, while minimizing normal tissue complications. Techniques for fluoroscopic gating without implanted fiducial markers have been developed in a classification framework. Due to the high-dimensionality nature of the images, dimensionality reduction techniques such as principal component analysis (PCA) were used to preprocess the data. In this work, we have applied generalized linear discriminant analysis (GLDA) to the respiratory gating problem. The fundamental difference from conventional dimensionality reduction techniques is that GLDA explicitly takes into account the label information available in the training set and therefore is efficient for discrimination among classes. On average, GLDA was demonstrated to perform similarly with PCA trained with SVM at high nominal duty cycles and outperform PCA in terms of classification accuracy (CA) and target coverage (TC) at lower nominal duty cycle (20%). A major advantage of GLDA is its robustness, while CA and TC using PCA can be reduced by up to 10% depending on the data dimensionality. With only 1-dimensional feature vectors, GLDA is much more computationally efficient than PCA. Therefore, GLDA is an effective and efficient method for respiratory gating with markerless fluoroscopic images.

Original languageEnglish (US)
Title of host publication8th International Conference on Machine Learning and Applications, ICMLA 2009
Pages468-472
Number of pages5
DOIs
StatePublished - Dec 1 2009
Event8th International Conference on Machine Learning and Applications, ICMLA 2009 - Miami Beach, FL, United States
Duration: Dec 13 2009Dec 15 2009

Publication series

Name8th International Conference on Machine Learning and Applications, ICMLA 2009

Other

Other8th International Conference on Machine Learning and Applications, ICMLA 2009
CountryUnited States
CityMiami Beach, FL
Period12/13/0912/15/09

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction
  • Software

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    Li, R., Lewis, J. H., & Jiang, S. B. (2009). Markerless fluoroscopic gating for lung cancer radiotherapy using generalized linear discriminant analysis. In 8th International Conference on Machine Learning and Applications, ICMLA 2009 (pp. 468-472). [5381464] (8th International Conference on Machine Learning and Applications, ICMLA 2009). https://doi.org/10.1109/ICMLA.2009.51