Computerized detection and classification of pulmonary pathologies from CT images: current approaches, challenges, and future trends
Radiological Society of North America 2014 Scientific Assembly and Annual Meeting
Abstract
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This RSNA 2014 education exhibit reviewed CT-based approaches to lung pathology segmentation and classification, including imaging features, machine-learning classifiers, and challenges such as image quality and analysis time.
Summary
Presented at the Radiological Society of North America (RSNA) 2014 Scientific Assembly and Annual Meeting, this education exhibit reviewed computerized methods for detecting, segmenting, and classifying pulmonary pathologies in computed tomography (CT) images. Its outline covered lung disease imaging patterns, pathology volume as a potential imaging marker, texture-feature selection, machine-learning classifiers, and challenges involving image quality, discriminative information, and analysis time. The RSNA archive lists the exhibit as presented on December 4, 2014, and records a Certificate of Merit.
Cite this work
Awais Mansoor; Ulas Bagci; Ziyue Xu; Brent Foster; Georgios Z. Papadakis; Ken Olivier; Jason M. Elinoff; Anthony F. Suffredini; Jayaram K. Udupa; Daniel Joseph Mollura (2014). Computerized detection and classification of pulmonary pathologies from CT images: current approaches, challenges, and future trends. Radiological Society of North America 2014 Scientific Assembly and Annual Meeting https://archive.rsna.org/2014/14018415.html
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