Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21818
Title: A multistage discriminative model for tumor and lymph node detection in thoracic images
Authors: Song, Y
Cai, W
Kim, J
Feng, DD
Keywords: Abnormal lymph node
detection
discriminative
lung tumor
multistage
spatial feature
Issue Date: 2012
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on medical imaging, 2012, v. 31, no. 5, 6134676, p. 1061-1075 How to cite?
Journal: IEEE transactions on medical imaging 
Abstract: Analysis of primary lung tumors and disease in regional lymph nodes is important for lung cancer staging, and an automated system that can detect both types of abnormalities will be helpful for clinical routine. In this paper, we present a new method to automatically detect both tumors and abnormal lymph nodes simultaneously from positron emission tomographycomputed tomography thoracic images. We perform the detection in a multistage approach, by first detecting all potential abnormalities, then differentiate between tumors and lymph nodes, and finally refine the detected tumors for false positive reduction. Each stage is designed with a discriminative model based on support vector machines and conditional random fields, exploiting intensity, spatial and contextual features. The method is designed to handle a wide and complex variety of abnormal patterns found in clinical datasets, consisting of different spatial contexts of tumors and abnormal lymph nodes. We evaluated the proposed method thoroughly on clinical datasets, and encouraging results were obtained.
URI: http://hdl.handle.net/10397/21818
ISSN: 0278-0062 (print)
1558-254X (online)
DOI: 10.1109/TMI.2012.2185057
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