Evaluation of Algorithms for Lung Fissure Segmentation in CT Images

Alexander Schmidt-Richberg, Jan Ehrhardt, Matthias Wilms, René Werner, Heinz Handels (Editor), Thomas Tolxdorff, Thomas Martin Deserno, Heinz Handels (Editor), Hans-Peter Meinzer


Automatic detection of the interlobular lung fissures is a crucial task in computer aided diagnostics and intervention planning, and required for example for determination of disease spreading or pulmonary parenchyma quantification. Moreover, it is usually the first step of a subsequent segmentation of the five lung lobes. Due to the clinical relevance, several approaches for fissure detection have been proposed. They aim at finding plane-like structures in the images by analyzing the eigenvalues of the Hessian matrix. Furthermore, these values can be used as features for supervised fissure detection. In this work, two approaches for supervised an three for unsupervised fissure detection are evaluated and compared to each other. The evaluation is based on thoracic CT images acquired with different radiation doses and different resolutions. The experiments show that each approach has advantages and the choice should be made depending on the specific requirements of following algorithm steps.
Original languageEnglish
Title of host publicationBildverarbeitung für die Medizin 2012
EditorsThomas Tolxdorff, Thomas Martin Deserno, Heinz Handels, Hans-Peter Meinzer
Number of pages6
PublisherSpringer Vieweg, Berlin Heidelberg
Publication date16.03.2012
Pages201 - 206
ISBN (Print)978-3-642-28501-1
ISBN (Electronic)978-3-642-28502-8
Publication statusPublished - 16.03.2012
EventWorkshop on Bildverarbeitung fur die Medizin 2012 - Berlin, Germany
Duration: 18.03.201220.03.2012


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