Journal of Northeastern University Natural Science ›› 2018, Vol. 39 ›› Issue (7): 942-948.DOI: 10.12068/j.issn.1005-3026.2018.07.007

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Method of Measuring the Benign and Malignancy of Pulmonary Nodules Based on ARG

ZHAO Hai, YANG Ting-ting, ZHU Hong-bo, DOU Sheng-chang   

  1. School of Computer Science & Engineering, Northeastern University, Shenyang 110169, China.
  • Received:2017-02-27 Revised:2017-02-27 Online:2018-07-15 Published:2018-07-11
  • Contact: YANG Ting-ting
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Abstract: Identification of benign and malignant pulmonary nodules is an important task during the diagnosis of lung cancer. Aimed for solving this problem, a method of measuring the malignancy of pulmonary nodules based on the attributed relational graph (ARG) was proposed. Feature structures of input lung nodule CT image patches were constructed with ARGs and the nodule category template was built by mining and-or graph (AoG) from ARGs in the proposed method. Moreover, Markov blanket discovering algorithm was applied for discriminative features selection to reduce the node number of ARGs, so the computational complexity of the graph matching for AoG mining was greatly reduced. Experimental results show that the recognition rate of malignant pulmonary nodules is up to 90.12%, thus the proposed method can help identify the benign and malignant pulmonary nodules accurately and rapidly.

Key words: ARG (attributed relational graph), and-or graph, Markov blanket, pulmonary nodule, benign and malignant

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