Automated interpretation of ventilation-perfusion lung scintigrams for the diagnosis of pulmonary embolism using support vector machines

  • Authors:

  • Anders Ericsson;Amelié Huart;Andreas Ekefjärd;Kalle Åström;Holger Holst;Eva Evander;Per Wollmer;Lars Edenbrandt

  • Affiliations:

  • Mathematics, Center for Mathematical Sciences, Institute of Technology, Lund University, Lund, Sweden;Mathematics, Center for Mathematical Sciences, Institute of Technology, Lund University, Lund, Sweden;Mathematics, Center for Mathematical Sciences, Institute of Technology, Lund University, Lund, Sweden;Mathematics, Center for Mathematical Sciences, Institute of Technology, Lund University, Lund, Sweden;Department of Clinical Physiology, Lund University, Lund, Sweden;Department of Clinical Physiology, Lund University, Lund, Sweden;Department of Clinical Physiology, Lund University, Lund, Sweden;Department of Clinical Physiology, Lund University, Lund, Sweden

  • Venue:
  • SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

Abstract

The purpose of this study was to develop a new completely automated method for the interpretation of ventilation-perfusion (V-P) lung scintigrams for the diagnosis of pulmonary embolism, A new way of extracting features, characteristic for pulmonary embolism is presented. These features are then used as input to a Support Vector Machine, which discriminates between pulmonary embolism or no embolism. Using a material of 509 training cases and 104 test cases, the performance of the system, measured as the area under the ROC curve, was 0.86 in the test group. It is concluded that a completely automatic method can be used for interpretation of V-P scintigrams. It is faster and more robust than a previously presented method [4, 5] and the accuracy is at the same level as the the previous method. It also handles abnormalities in the lunges.