Feature-based lung nodule classification

Authors:
Amal Farag;Asem Ali;James Graham;Shireen Elhabian;Aly Farag;Robert Falk
Affiliations:
Computer Vision and Image Processing Laboratory, University of Louisville, Louisville, KY;Computer Vision and Image Processing Laboratory, University of Louisville, Louisville, KY;Computer Vision and Image Processing Laboratory, University of Louisville, Louisville, KY;Computer Vision and Image Processing Laboratory, University of Louisville, Louisville, KY;Computer Vision and Image Processing Laboratory, University of Louisville, Louisville, KY;Jewish Hospital, Louisville, KY
Venue:
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Year:
2010

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Abstract

Model-based detection and classification of nodules are two major steps in CAD systems design and evaluation. This paper examines feature-based nodule description for the purpose of classification in low dose CT scanning. After candidate nodules are detected, a process of classification of these nodules into types is needed. The SURF and the LBP descriptors are used to generate the features that describe the texture of common lung nodules. These features were optimized and the resultant set was used for classification of lung nodules into four categories: juxta-pleural, well-circumscribed, vascularized and pleural-tail, based on the extracted information. Experimental results illustrate the efficiency of using multi-resolution feature descriptors, such as the SURF and LBP algorithms, in lung nodule classification.